System and method for educating users, including responding to patterns

ABSTRACT

Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.

INCORPORATION BY REFERENCE TO RELATED APPLICATION

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 15/148,757, filed May 6, 2016, which claims the benefit of U.S.Provisional Application No. 62/158,463, filed on May 7, 2015. Theaforementioned applications are incorporated by reference herein intheir entirety, and are hereby expressly made a part of thisspecification.

TECHNICAL FIELD

The present embodiments relate to continuous analyte monitoring, and, inparticular, to control of operation of an analyte monitor upon changesin available data in a continuous analyte monitoring system.

BACKGROUND OF THE INVENTION

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin-dependent) and/or in which insulinis not effective (Type II or non-insulin-dependent). In the diabeticstate, the patient or user suffers from high blood sugar, which cancause an array of physiological derangements associated with thedeterioration of small blood vessels, for example, kidney failure, skinulcers, or bleeding into the vitreous of the eye. A hypoglycemicreaction (low blood sugar) can be induced by an inadvertent overdose ofinsulin, or after a normal dose of insulin or glucose-lowering agentaccompanied by extraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes normally only measures his or her glucose levels two tofour times per day. Unfortunately, such time intervals are so far spreadapart that the person with diabetes likely finds out too late of ahyperglycemic or hypoglycemic condition, sometimes incurring dangerousside effects. It is not only unlikely that a person with diabetes willbecome aware of a dangerous condition in time to counteract it, but itis also likely that he or she will not know whether his or her bloodglucose concentration value is going up (higher) or down (lower) basedon conventional methods. Diabetics thus may be inhibited from makingeducated insulin therapy decisions.

Another device that some diabetics used to monitor their blood glucoseis a continuous analyte sensor, e.g., a continuous glucose monitor(CGM). A CGM typically includes a sensor that is placed invasively,minimally invasively or non-invasively. The sensor measures theconcentration of a given analyte within the body, e.g., glucose, andgenerates a raw signal using electronics associated with the sensor. Theraw signal is converted into an output value that is rendered on adisplay. The output value that results from the conversion of the rawsignal is typically expressed in a form that provides the user withmeaningful information, and in which form users have become familiarwith analyzing, such as blood glucose expressed in mg/dL.

Commercial CGM systems are designed for Type I patients and/orintensively managed insulin-dependent type II patients. These systemsare designed for accuracy and reliability, but are generally expensiveand complicated, requiring significant technical knowledge. Such systemscommonly provide more information than may be necessary for the broaderpopulation, e.g., type II patients on oral medications as well aspatients with pre-diabetes, gestational diabetes, and the like.

In addition, many such people are interested not only in control oftheir diabetes, or in reversing their progression toward diabetes, butalso in weight loss, optimizing sports regimes, including participationin performance sports, optimizing diet and food intake, and otheraspects. Indeed, such monitoring may be important factors in reversingthe progression toward diabetes. While certain current systems allowusers to enter data about meals and exercise, such are not integratedinto the rest of the monitoring ecosystem, and such systems lack boththe technical integration as well as useful ways to utilize suchintegrated knowledge. In addition, active input of such variables byusers is low.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY OF THE INVENTION

Systems and methods according to present principles provide convenientways in which users may learn and become familiar with the effects ofvarious aspects of their lifestyle on their health. Users may learnabout how food and/or exercise affects their glucose level and otherphysiological parameters, as well as overall health. In some cases, theuser selects a program or a challenge to try; in other cases, acomputing environment embodying the system suggests programs to try,including on the basis of pattern recognition, i.e., by the computingenvironment determining how a user could improve a detected pattern insome way. In this way, users such as type II diabetics or even users whoare nondiabetic or prediabetic may learn healthy habits to benefit theirhealth. Systems and methods according to present principles address thetechnical limitations of current systems; in particular, that suchsystems are not optimized to provide the most effective monitoring toType II individuals or other more casual users. More specifically, priorsystems cannot provide programmatic learning. A system that providesprogrammatic coaching and self-learning can better engage users and be amore effective tool to achieve the desired benefit and clinicaloutcomes. Such effectiveness can also reduce certain inefficiencies,causing the computing environment implementing the monitoring to requirefewer cycles, causing less battery drain, and so on.

Continuous analyte sensing can also be applied beyond diabetics togeneral weight loss, wellness, sports optimization, medicationmanagement and more. Applications of the systems and methods describedare also applicable for non-type-I users, athletes or other users whodesire to optimize their training for sports and fitness endeavors.Other applications include weight loss optimization. It will beunderstood that the systems and methods according to present principlesmay be applied to any users, including Type I diabetics, Type IIdiabetics, pre-diabetics, those with gestational diabetes,non-diabetics, users with interests in optimizing sports or fitnessroutines or eating habits, users interested in losing weight orotherwise increasing their health, or indeed any other user interestedin bettering their health or learning more about how their habits andactions affect their health. Other applications will be understood giventhe description herein.

Systems and methods according to present principles may also be employedto ease the input of event data such as meal and exercise data, makingentry of the same more convenient to type II users. In particular, suchmay be passively determined in a reliable either through additionalsensors, e.g., lactate sensor, accelerometer, or via analytics, e.g.,pattern recognition of a meal combined with geolocation data.

In a first aspect, the embodiments are directed towards a method ofevaluating a user against a program, including: displaying a userinterface, the user interface including one or more graphical elementsrepresenting respective programs, the one or more programs configured toguide a user in treating diabetes; receiving a selection of a programfrom the user interface; monitoring and storing glucose concentrationdata of the user; analyzing the monitored and stored glucoseconcentration data of the user; evaluating the analyzed glucoseconcentration data of the user against the selected program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following. The displayinga user interface may further include analyzing retrospective data of auser and basing the one or more graphical elements at least in part onthe analysis. The method may further include, after the receiving aselection of a program, displaying an indication on the user interface,the indication representing initial guidance for following the program.The displaying an indication may include displaying suggested meals,foods, or recipes, helpful in performing the program; and/or displayingsuggested exercise routines helpful in performing the program. Themethod may further include, after the receiving a selection of aprogram: displaying one or more graphical elements representingrespective subprograms, the one or more subprograms configured tofurther guide a user in treating the disease; and receiving a selectionof a selected subprogram. The monitoring and storing may be performed bya continuous glucose monitor. The method may further include, monitoringother data about the user, and the analyzing or evaluating steps, orboth, may be based on the glucose concentration data and the other data.The other data may include activity data, such as where the activitydata is received from an accelerometer or GPS device. The activity datamay indicate an activity level, where the activity level is selectedfrom the group consisting of: sleeping, sedentary, light activity,medium activity, or strenuous activity. The evaluating may includeevaluating an effect of the activity data on the glucose concentrationdata. The displaying may include displaying the effect of the activitydata on the glucose concentration data. The data may include data aboutone or more other analytes, such as ketones, lactic acid, lactate,glycerol, triglycerides, cortisol, and testosterone.

The glucose concentration data may be measured by a glucose sensor, andthe data about one or more other analytes may be received from one ormore other analyte sensors, and the one or more other analyte sensorsmay be calibrated based on a calibration of the glucose sensor.

The other data may include meal data, e.g., received from a socialnetwork, user entry, a food app, or photographic data, or inferred fromknown parameters of the individual and their CGM curve. Meal data mayalso be inferred beforehand in real time by scanning a meal.

The method may further include, following the receiving of meal data:querying a user to enter a prediction of a measured glucoseconcentration value following a duration of time; receiving user inputof the prediction; subsequent to the duration of time, displaying anindication of the prediction and an actual current measured glucoseconcentration value.

The analyzing and evaluating may include determining a glycemic impactof the meal data, and the displaying may include displaying the glycemicimpact of different meals. The displaying the glycemic impact ofdifferent meals may include displaying the different meals in order fromlowest to highest or highest to lowest of glycemic impact. Theevaluating may include evaluating the effect of the meal data on theglucose concentration data. The evaluating may include comparing theglucose concentration value over time with data about typical or actualmealtimes of the user. The displaying may include displaying an effectof the meal data on the glucose concentration data. The user interfacemay include at least one graphical element pertaining to a meal programand at least one graphical element pertaining to an exercise program. Ifthe receiving a selection of a program includes receiving a selection ofa meal program, then the method may further include displaying one ormore graphical elements representing respective subprograms pertainingto different meals, the one or more subprograms configured to furtherguide a user in treating the disease. The method may further includereceiving a selection of one of the respective subprograms, such thatthe user is instructed to vary a meal choice for a particular meal overa predetermined time period, such as one week.

If the receiving a selection of a program includes receiving a selectionof an exercise program, then the method may further include displayingone or more graphical elements representing respective subprogramspertaining to different exercise programs, the one or more subprogramsconfigured to further guide a user in treating the disease. The methodmay further include receiving a selection of one of the respectivesubprograms, such that the user is instructed to perform an exerciseactivity of a predetermined intensity level and/or duration over apredetermined time period. If the receiving a selection of a programincludes receiving a selection of an exercise program, then the methodmay further include: receiving retrospective user data; analyzing thereceived retrospective user data to determine if a prior user activitysimilar to the exercise program has been performed, and if a glycemicevent has occurred after such determined prior user activity; if aglycemic event has occurred after such determined prior user activity,then determining a suggestion which may be employed to counteract aneffect of such glycemic event; and displaying an indication of thesuggestion as initial guidance. The monitoring other data may includemonitoring metabolism data.

The monitoring of other data may include receiving data from acloud-connected source, and the cloud-connected source may be a socialnetwork, a caregiver network, or a network of diabetes patients. Theselected program may be associated with a difficulty level, and theevaluating against the selected program may include evaluating againstthe associated difficulty level. The difficulty level may be set by theprogram or by the user.

The output may include a color indicating if a goal associated with theprogram was met. The output may include an avatar indicating if a goalassociated with the program was met. The output may include a trendgraph indicating at least a trace signal representing the glucoseconcentration value over a time period associated with the program. Thetrend graph further may include a desired glucose concentration value orrange of values over the time period. The desired glucose concentrationvalue or range of values may be based on a modeled, ideal, or predictedglucose concentration value or range of values. The selected program maybe associated with a difficulty level, and the desired glucoseconcentration value or range of values may be further based on thedifficulty level. The method may further include transmitting the outputto a cloud connected entity. The method may further include, prior to adisplaying of an actual current measured glucose concentration value,querying a user to enter a prediction of the current glucoseconcentration value; receiving user input of the prediction; anddisplaying an indication of the prediction and the actual currentmeasured glucose concentration value. The output may include anindication of a predicted or projected A1C value.

The method may further include determining if the actual currentmeasured glucose concentration value meets predefined criteria; and ifthe predefined criteria is met, displaying an indication that thepredefined criteria has been met. The predefined criteria may be athreshold glucose concentration value or a projected average glucoselevel or projected A1C.

In a second aspect, the embodiments are directed towards a method ofalerting a user to a pattern, and providing a program to address thepattern, including: evaluating user data to determine a pattern;comparing the pattern against a criteria to determine if the determinedpattern is a pattern for which improvement is desired; determining aprogram to improve the pattern; monitoring and storing glucoseconcentration data of the user; analyzing the monitored and storedglucose concentration data of the user; evaluating the analyzed glucoseconcentration data of the user against the determined program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following.

The determining a program to improve the pattern may further include:determining a set of potential programs to improve the pattern;displaying a user interface, the user interface including one or moregraphical elements respectively representing the set of potentialprograms; and receiving a selection of one of the potential programsfrom the user interface, where the determined program is defined as thereceived selection.

The displaying a user interface may further include analyzingretrospective data of a user and basing the user interface at least inpart on the analysis. The method may further include, after the step ofdetermining a program, displaying an indication on the user interface,the indication representing initial guidance for following thedetermined program. The displaying an indication may include: displayingsuggested meals, foods, or recipes, helpful in performing the program;and/or displaying suggested exercise routines helpful in performing theprogram. The monitoring and storing may be performed at least in part bya continuous glucose monitor. The interface may further allow foranalyzing potential meals, activities, or actions, in real time, andproviding insight on what would happen if the action were taken.

The method may further include monitoring other data about the user, andthe analyzing or evaluating steps, or both, may be based on the glucoseconcentration data and the other data. The other data may includeactivity data, such as may be received from an accelerometer or a GPSdevice. The activity data may indicate an activity level, and theactivity level may be selected from the group consisting of: sleeping,sedentary, light activity, medium activity, or strenuous activity. Thecriteria may be received from a cloud-connected source. The evaluatingmay include evaluating an effect of the activity data on the glucoseconcentration data. The displaying may include displaying the effect ofthe activity data on the glucose concentration data. The other data mayinclude data about other analytes, such as ketones, lactic acid,lactate, glycerol, triglycerides, cortisol, and testosterone. Theglucose concentration data may be measured by a glucose sensor, and thedata about one or more other analytes may be received from one or moreother analyte sensors, and the one or more other analyte sensors may becalibrated based on a calibration of the glucose sensor.

The other data may include meal data, such as may be received from asocial network, user entry, a food app, or photographic data. Theanalyzing and evaluating may include determining a glycemic impact ofthe meal data, and the displaying may include displaying the glycemicimpact of different meals. The evaluating may include evaluating aneffect of the meal data on the glucose concentration data.

The displaying may include displaying the effect of the meal data on theglucose concentration data, both retrospectively and prospectively(prospectively being the ability to predict a response before ithappens). The user interface may include at least one graphical elementpertaining to a meal program and at least one graphical elementpertaining to an exercise program. If the receiving a selection of apotential program includes receiving a selection of a potential mealprogram, then the method may further include determining the program tobe a meal program. The meal program may be configured to instruct theuser to vary a meal choice for a particular meal over a predeterminedtime period, e.g., one week. If the receiving a selection of a potentialprogram includes receiving a selection of a potential exercise program,then the method may further include determining the program to be anexercise program. The exercise program may be configured to instruct theuser to perform an exercise activity of a predetermined intensity leveland duration over a predetermined time period, such as over one week.The monitoring other data may include receiving data from a cloudconnected source, such as a social network, a caregiver network, or anetwork of patients. The determined program may be associated with adifficulty level, and the evaluating against the determined program mayinclude evaluating against the associated difficulty level. Thedifficulty level may be set by the program based at least in part on aretrospective history of the patient. The difficulty level may beselected by the user.

The output may include a color indicating if a goal associated with theprogram was met. The output may include an avatar indicating if a goalassociated with the program was met. The output may include a trendgraph indicating at least a trace signal representing the glucoseconcentration value over a time period associated with the program. Thetrend graph further may include a desired glucose concentration value orrange of values over the time period. The desired glucose concentrationvalue or range of values may be based on a modeled, ideal, or predictedglucose concentration value or range of values. The determined programmay be associated with a difficulty level, and the desired glucoseconcentration value or range of values may be further based on thedifficulty level. The evaluated user data may include retrospectiveglucose concentration data. The evaluated user data may includeretrospective meal data. The displaying an output may include displayingan indicator of the determined pattern. The determined pattern may beselected from the group consisting of: overnight lows, postprandialspikes, a type of discriminated fault, a pattern of high glucosevariability, a consistent pattern of weekly highs or lows. The methodmay further include determining a baseline glucose concentration patternfor the user, and the determined pattern may be a consistent variationfrom the baseline pattern.

The evaluating user data to determine a pattern step may further includeinitiating a discovery mode, where in the discovery mode, one or morequestions are posed on the user interface, user responses to the one ormore questions constituting additional user data, and the evaluatinguser data step further may further include evaluating the additionaluser data along with the monitored and stored glucose concentration datato determine a pattern.

The additional user data may be meal data, activity data, and so on. Themethod may further include transmitting the output to a cloud connectedentity.

In a third aspect, the embodiments are directed towards a method ofoptimizing a diabetes therapy involving a medicament, including:receiving data about a first medicament to be ingested by a user in afirst regimen; monitoring and storing glucose concentration data of theuser; analyzing the monitored and stored glucose concentration data ofthe user; evaluating the analyzed glucose concentration data of the useragainst the first regimen; and displaying an output responsive to theevaluating step.

Implementations may include one or more of the following.

The method may further include receiving data from a cloud connectedsource, the data pertaining to medicaments and/or regimens attempted bya plurality of other users, the plurality of other users determined byhaving similar demographics to the user, such that the diabetes therapyis optimized for the user in an efficient way. The similar demographicsmay include one or more similar demographic parameters selected from thegroup consisting of: age, ethnicity, weight, BMI, and type of diabetes.The evaluating may include determining either the first medicament orthe first regimen to be non-optimal for the user, and further including:determining a second medicament or a second regimen, or both, for theuser; and displaying an output indicating the second medicament, or thesecond regimen, or both. The regimen may include data about a dosage ofa respective medicament and the timing of ingestion of the respectivemedicament.

The method may further include monitoring other data about the user, andthe analyzing or evaluating steps, or both, may be based on the glucoseconcentration data and the other data. The other data may includeactivity data, meal data, or data about other analytes, and the mealdata may be received from sources described above. The other data mayalso include data about job type, education status, age, sex, maritalstatus, buying preferences, ethnic background, history of heart disease,whether the user is a smoker, and so on, all of which being significantpredictors of diabetes risk. The data about other analytes may be aboutthe analytes described above. The method may further include determininguser compliance with the regimen. The method may further includetransmitting an indication of the output to a cloud connected entity,along with demographic data about the user.

In a fourth aspect, the embodiments are directed towards a method ofoptimizing user activity for sports performance, including: displaying auser interface, the user interface including one or more graphicalelements representing respective programs, the one or more programsconfigured to guide a user in optimizing an exercise routine; receivinga selection of a program from the user interface; monitoring and storingactivity data of the user; monitoring and storing an analyteconcentration data of the user; analyzing the monitored and storedactivity and analyte concentration data of the user; evaluating theanalyzed data against the selected program; and displaying an outputresponsive to the evaluating step.

Implementations may include one or more of the following.

The activity data may indicate an activity level as stratified orcategorized in the levels noted above. The analyte may be selected fromthe group consisting of: glucose, lactic acid, lactate, ketones,glycerol, testosterone, cortisol, and combinations thereof. In somecases two analytes may be measured, a first analyte and a secondanalyte, the first analyte being glucose measured by a glucose sensor,and the second analyte may be measured by an analyte sensor calibratedbased on a calibration of the glucose sensor. In another implementation,the first analyte may be glucose measured by a glucose sensor, and thesecond analyte may be lactate measured by a lactate sensor. The lactatesensor may be calibrated based on the glucose sensor.

The method may further include monitoring a parameter indicative of ametabolic level of the user, and the evaluating may further includeevaluating the metabolic level along with the analyzed data. Theactivity data may be received from an accelerometer or GPS. The activitydata may be received from an exercise machine or a heart rate monitor orthe like. The optimizing an exercise routine may include optimizing abuilding of muscle mass. The optimizing an exercise routine may includeoptimizing cardiovascular health. The method may further includemonitoring other user data, and the evaluating step may further includeevaluating the other user data along with the analyzed data. The otherdata may include a ratio of VCO2 and VO2. The other data may alsoinclude meal data received from sources noted above. The displaying mayinclude displaying the effect of the meal data on the exercise routineto be optimized. The selected program may include a goal havingpredetermined criteria, and the evaluating step may include determiningif the analyzed data meets or matches the predetermined criteria. Thepredetermined criteria may include an envelope of acceptable analytetraces. The analyzed data may be represented by a trace graph, and thepredetermined criteria may include whether the trace graph is within theenvelope of acceptable analyte traces. The selected program may beassociated with a difficulty level, and the evaluating against theselected program may include evaluating against the associateddifficulty level. The difficulty level may be set by the program or bythe user. The output may include a color or avatar indicating a level ofoptimization. The output may include a trace graph indicating a level ofoptimization. The output may include a column, row, or tachometerdiagram having a needle pointing at a range corresponding to the output.Where the output indicates metabolism, the diagram may have ranges of:calorie intake greater than calorie expenditure; and calorie intake lessthan calorie expenditure. The output may indicate whether the metabolismis fat burning or carbohydrate burning. The output may indicateproximity to a lactate threshold. The method may further includetransmitting an indication of the output to a cloud connected entity.

In a fifth aspect, the embodiments are directed towards a system forsports optimization, including: a sports sensor; a sports transmitter;and a sports receiver, the sports receiver configured to perform themethods described above.

Implementations may include one or more of the following.

The kit may further include a circuit for determination of activitydata. The circuit may include an accelerometer or a module fordetermination of location data using GPS. The circuit may include areceiver for receiving activity data from an external source, such as amobile device. The circuit may be disposed within the sports receiver.The circuit may be disposed within the sports transmitter. The analytemay be selected from the group consisting of: glucose, lactic acid,lactate, ketones, glycerol, and combinations thereof.

In a sixth aspect, the embodiments are directed towards a method ofoptimizing weight loss, including: displaying a user interface, the userinterface including one or more graphical elements representingrespective programs, the one or more programs configured to guide a userin optimizing weight loss; receiving a selection of a program from theuser interface; monitoring and storing meal data of the user; monitoringand storing an analyte concentration data of the user; analyzing themonitored and stored meal data and analyte concentration data of theuser; evaluating the analyzed data against the selected program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following.

The method may further include monitoring and storing activity data ofthe user, and where the evaluating further may include evaluating theactivity data along with the analyzed data. The analyte may be selectedfrom the group consisting of: glucose, insulin lactic acid, lactate,ketones, glycerol, and combinations thereof. Two analytes may bemeasured, a first analyte and a second analyte, the first analyte beingglucose measured by a glucose sensor, and the second analyte measured byan analyte sensor calibrated based on a calibration of the glucosesensor. The first analyte may be glucose and may be measured by aglucose sensor, and the second analyte may be insulin and may bemeasured by an insulin sensor, and the output may indicate a measure ofcalories ingested and calories expended. The method may further includemonitoring a parameter indicative of a metabolic level of the user, andthe evaluating further may include evaluating the metabolic level alongwith the analyzed data. The monitoring a parameter indicative of ametabolic level may include receiving data from a metabolic sensor. Themetabolic sensor may measure an analyte involved in conversion of foodinto fat storage or intermediaries between dietary inputs and fat. Theanalyte involved in conversion of food into fat storage orintermediaries between dietary inputs and fat may be selected from thegroup consisting of: glucose, glucagon, insulin, glycogen, starch, freefatty acid, triglycerides, monoglycerides, proteins involved in fatstorage, glycerol, pyruvate, lipids, acetyl Co A, intermediates in thecitric acid cycle, ketone bodies including acetone, acetoacetic acid,and beta hydroxybutyric acid, lactate, molecules involved in aerobic oranaerobic metabolic pathways, or combinations of these. The activitydata may indicate an activity level, and the activity level may bestratified or categorized as noted above. The activity data may bereceived from an accelerometer or GPS, from an exercise machine, from aheart rate monitor, and so on. The optimizing weight loss may includeoptimizing a metabolic rate. The optimizing weight loss may includeoptimizing fat consumption. The meal data may be received from sourcesnoted above.

The displaying may include displaying the effect of the meal data on theweight loss to be optimized. The selected program may include a goalhaving predetermined criteria, and the evaluating step may includedetermining if the analyzed data meets or matches the predeterminedcriteria. The method may further include monitoring and storingmetabolic level data of the user, and the predetermined criteria mayinclude an envelope of acceptable metabolic levels. The metabolic leveldata may be represented by a trace graph, and the predetermined criteriamay include whether the trace graph is within the envelope of acceptablemetabolic levels. The method may include displaying initial guidance,the initial guidance based on the selected program. The monitoring andstoring an analyte concentration data of the user may further includemonitoring and storing glucose or lactic acid concentration data, orboth. Where the selected program is to lose a predetermined number ofpounds, the method may further include receiving data about a weight ofthe user prior to the program, and the evaluating step may evaluate acurrent weight of the user and the weight of the user prior to theprogram against the predetermined number of pounds. Where the selectedprogram is to lose a predetermined percentage of body fat, the methodmay further include receiving data about a body fat percentage of theuser prior to the program, and the evaluating step may evaluate acurrent body fat percentage of the user and the body fat percentage ofthe user prior to the program against the predetermined percentage ofbody fat. Where the selected program is to achieve a predeterminedmetabolic level over a duration of time, the method may further includereceiving data about a metabolic level over the duration of time, andthe evaluating step may evaluate the metabolic level over the durationof time against the predetermined metabolic level over the duration oftime. Where the selected program is to achieve a predetermined fatconsumption level over a duration of time, the method may furtherinclude receiving a fat consumption level over the duration of time, andthe evaluating step may evaluate the fat consumption level over theduration of time against the predetermined fat consumption level overthe duration of time.

The receiving data about a fat consumption level may include: receivingdata selected from the group consisting of: a lactate level and a heartrate, a ratio of VO2 and VCO2, a level of glycerol, a level of ketones,or a level of free fatty acids, or a combination of the above,associated with the user; and determining a fat consumption level fromthe received data. The determined fat consumption level may indicate asource of calorie expenditure, from fat or from carbohydrates.

The receiving data about a fat consumption level may include receivingdata about a lactate level and data from an accelerometer, and thelactate level and the accelerometer data may be evaluated against theselected program to determine if the predetermined fat consumption levelhas been achieved. Where the selected program is to achieve apredetermined energy expenditure, the method may further includereceiving data about a current energy expenditure, and the evaluatingstep may evaluate the current energy expenditure against thepredetermined energy expenditure. The selected program may be associatedwith a difficulty level, and the evaluating against the selected programmay include evaluating against the associated difficulty level. Thedifficulty level may be set by the program or by the user. The outputmay include a color or an avatar or a trace graph indicating a level ofoptimization. The output may also include a column, row, or tachometerdiagram having a needle pointing at a range corresponding to the output.Where the output indicates metabolism, the diagram may have ranges of:calorie intake greater than calorie expenditure; and calorie intake lessthan calorie expenditure. The method may further include transmitting anindication of the output to a cloud-connected entity. The method mayfurther include displaying multiple parameters on the user interface,the multiple parameters including at least two of the group consistingof: fat consumption, percentage of max fat consumption, lactate level,or total fat consumption during workout.

In an seventh aspect, the embodiments are directed towards a method ofusing retrospective data to determine guidance on a user interface,including: evaluating retrospective user data to determine a suboptimaldata arrangement in an analyte concentration value of a user;determining a program to improve the suboptimal data arrangement;displaying an indication of the determined program on a user interface;receiving an indication to start the determined program entered by auser on the user interface; monitoring and storing analyte concentrationdata of the user; evaluating the monitored and stored analyteconcentration data of the user against the determined program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following.

The suboptimal data arrangement may constitute a pattern, and thedetermining a program may be initiated upon an unambiguous determinationthat a pattern exists. The analyte may be glucose.

In an eighth aspect, the embodiments are directed towards a method ofoptimizing exercise, including: displaying a user interface, the userinterface including one or more graphical elements representingrespective programs, the one or more programs configured to guide a userin optimizing weight loss; receiving a selection of a program from theuser interface; monitoring and storing activity data of the user;monitoring and storing lactate concentration data of the user; analyzingthe monitored and stored activity data and lactate concentration data ofthe user; evaluating the analyzed data against the selected program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following.

The method may further include displaying an output indicating if theuser is in a carbohydrate-burning zone. The method may further includedisplaying an output indicating how close a user is to a lactatethreshold. The method may further include: monitoring and storingglucose concentration data of the user; analyzing the monitored andstored glucose concentration data along with the monitored and storedactivity data and lactate concentration data; evaluating the analyzeddata including the glucose concentration data against the selectedprogram; and displaying an output responsive to the evaluating step. Themethod may further include displaying an output indicative of a sourceof calories expended, including whether from fat or from carbohydrates.

In a ninth aspect, the embodiments are directed towards a method ofdetermining net calories relative to a user, including: displaying auser interface, the user interface including one or more graphicalelements representing respective programs, the one or more programsconfigured to guide a user in optimizing weight loss; receiving aselection of a program from the user interface; monitoring and storing aglucose concentration data of the user; monitoring and storing aninsulin concentration data of the user; analyzing the monitored andstored glucose and insulin concentration data of the user to determine anumber of calories ingested and a number of calories expended; anddisplaying an output responsive to the analyzing step.

Implementations may include one or more of the following.

The method may further include monitoring and storing data correspondingto fat content, and analyzing the monitored and stored datacorresponding to fat content along with the glucose and insulinconcentration data. The fat content data may correspond to data aboutketones, glycerol or triglycerides.

In an tenth aspect, the embodiments are directed towards a method ofoptimizing weight loss, including: displaying a user interface, the userinterface including one or more graphical elements representingrespective programs, the one or more programs configured to guide a userin optimizing weight loss; receiving a selection of a program from theuser interface; monitoring and storing activity data of the user;monitoring and storing lactate concentration data of the user; analyzingthe monitored and stored activity data and lactate concentration data ofthe user; evaluating the analyzed data against the selected program; anddisplaying an output responsive to the evaluating step, where thedisplaying may include indicating to a user if the user is in a fatburning zone.

Implementations may include one or more of the following.

The method may further include displaying an output indicating a valueselected from the group consisting of: a fat burning rate, acarbohydrate-burning rate, a total fat expended, a total carbohydratesexpended, a calorie burning rate, a total calories expended, andcombinations thereof.

In a eleventh aspect, the embodiments are directed towards a sensorarray for measuring an analyte concentration, the sensor arrayincluding: a plurality of sensor devices each configured for insertionthrough the skin, where each sensor device may include a sensor unit anda mounting unit configured to support the sensor device on an exteriorsurface of the host's skin, the sensor unit including an in vivo portionhaving a tissue piercing element and a sensor body, the sensor bodyincluding at least one electrode and a membrane covering at least aportion of the at least one electrode; where the plurality of sensordevices may include a first sensor device and a second sensor device,where the first sensor device may include a first sensor configured tomeasure a first analyte, and where the second sensor device may includea second sensor configured to measure a second analyte; and where thesecond sensor device is calibrated using a calculation based on at leasta calibration parameter of the first sensor device.

Implementations may include one or more of the following.

Where one or more calibration parameters of the second sensor devicebear a known relationship with one or more calibration parameters of thefirst sensor device, calibration parameters of the second sensor devicemay be determined based on the calibration parameters of the firstsensor device. The calibration parameters may include a sensitivity ofthe sensor device. The calculation may be a linear calculation. Thefirst sensor device may be configured to measure glucose and the secondsensor device may be configured to measure an analyte selected from thegroup consisting of: ketones, triglycerides, glycerol, lactate, lacticacid, cortisol, testosterone, and combinations thereof. The first sensordevice may be configured to measure uric acid and the second sensordevice may be configured to measure glucose. The first sensor device maybe configured to measure glucose and the second sensor device may beconfigured to measure an analyte selected from the group consisting of:glucagon, insulin, other hormones involved in metabolic processes,glycogen, starch, free fatty acids, triglycerides, monoglycerides,troponin, cholesterol, proteins involved in fat storage, glycerol,pyruvate, lipids, other carbohydrates, molecules involved in breakingdown fat, glucagon, acetyl Co A, triglycerides, fatty acids,intermediaries in the citric acid cycle, ketone bodies, acetone,acetoacetic acid, beta hydroxybutyric acid, lactate, molecules involvedin aerobic or anaerobic metabolic pathways, or combinations of theabove. The first sensor device may be configured to be calibrated usinga blood measurement.

In a thirteenth aspect, the embodiments are directed towards a method ofevaluating a user against a program, including: displaying a userinterface, the user interface including one or more graphical elementsrepresenting respective programs, the one or more programs configured toguide a user in treating diabetes; receiving a selection of a programfrom the user interface; monitoring and storing analyte concentrationdata of the user; analyzing the monitored and stored analyteconcentration data of the user; evaluating the analyzed analyteconcentration data of the user against the selected program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following. The displayinga user interface may further include analyzing retrospective data of auser and basing the one or more graphical elements at least in part onthe analysis. The method may further include, after the receiving aselection of a program, displaying an indication on the user interface,the indication representing initial guidance for following the program.The displaying an indication may include: displaying suggested meals,foods, or recipes, helpful in performing the program; and/or displayingsuggested exercise routines helpful in performing the program. Themethod may further include, after the receiving a selection of aprogram: displaying one or more graphical elements representingrespective subprograms, the one or more subprograms configured tofurther guide a user in treating a physiological condition; andreceiving a selection of a selected subprogram. The monitoring andstoring may be performed by a continuous analyte monitor. The method mayfurther include monitoring other data about the user, and the analyzingor evaluating steps, or both, may be based on the analyte concentrationdata and the other data. The other data may include activity data, suchas may be received from an accelerometer or a GPS device. The activitydata may indicate an activity level, and the same may be selected fromthe group consisting of: sleeping, sedentary, light activity, mediumactivity, or strenuous activity.

The evaluating may include evaluating an effect of the activity data onthe analyte concentration data. The displaying may include displayingthe effect of the activity data on the analyte concentration data. Theother data may include data about one or more other analytes, and theother analytes may be selected from the group consisting of: ketones,lactic acid, lactate, glycerol, triglycerides, cortisol, andtestosterone.

The analyte concentration data may be measured by an analyte sensor, andthe data about one or more other analytes may be received from one ormore other analyte sensors. The one or more other analyte sensors may becalibrated based on a calibration of the analyte sensor. The other datamay also include meal data, such as may be received from one or more ofthe group selected from: a social network; user entry; a food app; orphotographic data. The method may further include, following thereceiving of meal data: querying a user to enter a prediction of ameasured analyte concentration value following a duration of time;receiving user input of the prediction; and subsequent to the durationof time, displaying an indication of the prediction and an actualcurrent measured analyte concentration value.

The user interface may include at least one graphical element pertainingto a meal program and at least one graphical element pertaining to anexercise program. If the receiving a selection of a program includesreceiving a selection of a meal program, the method may further includedisplaying one or more graphical elements representing respectivesubprograms pertaining to different meals, the one or more subprogramsconfigured to further guide a user in treating the disease. The methodmay further include receiving a selection of one of the respectivesubprograms, whereby the user is instructed to vary a meal choice for aparticular meal over a predetermined time period, e.g., one week.

If the receiving a selection of a program includes receiving a selectionof an exercise program, the method may further include displaying one ormore graphical elements representing respective subprograms pertainingto different exercise programs, the one or more subprograms configuredto further guide a user in treating the disease. The method may furtherinclude receiving a selection of one of the respective subprograms,whereby the user is instructed to perform an exercise activity of apredetermined intensity level and/or duration over a predetermined timeperiod, e.g., one week.

If the receiving a selection of a program includes receiving a selectionof an exercise program, the method may further include: receivingretrospective user data; analyzing the received retrospective user datato determine if a prior user activity similar to the exercise programhas been performed, and if a physiological event has occurred after suchdetermined prior user activity; if a physiological event has occurredafter such determined prior user activity, then determining a suggestionwhich may be employed to counteract an effect of such physiologicalevent; and displaying an indication of the suggestion as initialguidance.

The monitoring other data may include monitoring metabolism data. Themonitoring other data may also include receiving data from a cloudconnected source, such as a social network, a caregiver network, or anetwork of diabetes patients or a support group.

The selected program may be associated with a difficulty level, and theevaluating against the selected program may include evaluating againstthe associated difficulty level. The difficulty level may be set by theprogram or by the user.

The output may include a color indicating if a goal associated with theprogram was met or is on track to being met. The output may also oralternatively include an avatar indicating if a goal associated with theprogram was met. The output may include a trend graph indicating atleast a trace signal representing the analyte concentration value over atime period associated with the program. The trend graph may include adesired analyte concentration value or range of values over the timeperiod. The desired analyte concentration value or range of values maybe based on a modeled, ideal, or predicted analyte concentration valueor range of values. The selected program may be associated with adifficulty level, and the desired analyte concentration value or rangeof values may be further based on the difficulty level.

The method may include transmitting the output to a cloud connectedentity.

The method may further include, prior to a displaying of an actualcurrent measured analyte concentration value, querying a user to enter aprediction of the current analyte concentration value; receiving userinput of the prediction; and displaying an indication of the predictionand the actual current measured analyte concentration value.

The method may further include: determining if the actual currentmeasured analyte concentration value meets predefined criteria; and ifthe predefined criteria is met, displaying an indication that thepredefined criteria has been met. The predefined criteria may be athreshold analyte concentration value.

In a fourteenth aspect, the embodiments are directed towards a method ofalerting a user to a pattern, and providing a program to address thepattern, including: evaluating user data to determine a pattern;comparing the pattern against a criteria to determine if the determinedpattern is a pattern for which improvement is desired; determining aprogram to improve the pattern; monitoring and storing analyteconcentration data of the user; analyzing the monitored and storedanalyte concentration data of the user; evaluating the analyzed analyteconcentration data of the user against the determined program; anddisplaying an output responsive to the evaluating step.

Implementations may include one or more of the following. Thedetermining a program to improve the pattern may further include:determining a set of potential programs to improve the pattern;displaying a user interface, the user interface including one or moregraphical elements respectively representing the set of potentialprograms; receiving a selection of one of the potential programs fromthe user interface, where the determined program is defined as thereceived selection.

The displaying a user interface may further include analyzingretrospective data of a user and basing the user interface at least inpart on the analysis. The method may further include, after the step ofdetermining a program, displaying an indication on the user interface,the indication representing initial guidance for following thedetermined program. The displaying an indication may include: displayingsuggested meals, foods, or recipes, helpful in performing the program;and/or displaying suggested exercise routines helpful in performing theprogram.

The monitoring and storing may be performed at least in part by acontinuous analyte monitor. The method may further include monitoringother data about the user, and the analyzing or evaluating steps, orboth, may be based on the analyte concentration data and the other data.The other data may include activity data. The activity data may bereceived from an accelerometer or GPS device, and may indicate anactivity level, e.g., an activity level selected from the groupconsisting of: sleeping, sedentary, light activity, medium activity, orstrenuous activity.

The criteria may be received from a cloud-connected source. Theevaluating may include evaluating an effect of the activity data on theanalyte concentration data. The displaying may include displaying theeffect of the activity data on the analyte concentration data. The otherdata may include data about other analytes, such as: ketones, lacticacid, lactate, glycerol, triglycerides, cortisol, and testosterone. Theanalyte concentration data may be measured by an analyte sensor, and thedata about one or more other analytes may be received from one or moreother analyte sensors, and the one or more other analyte sensors may becalibrated based on a calibration of the analyte sensor.

The other data may include meal data, which may be received from: asocial network; user entry; a food app; or photographic data.

The determined program may be associated with a difficulty level, andthe evaluating against the determined program may include evaluatingagainst the associated difficulty level. The difficulty level may be setby the program based at least in part on a retrospective history of thepatient, or selected by the user.

The output may include a color indicating if a goal associated with theprogram was met. The output may also include an avatar indicating if agoal associated with the program was met. The output may include a trendgraph indicating at least a trace signal representing the analyteconcentration value over a time period associated with the program. Thetrend graph may include a desired analyte concentration value or rangeof values over the time period, where the desired analyte concentrationvalue or range of values is based on a modeled, ideal, or predictedanalyte concentration value or range of values. The determined programmay be associated with a difficulty level, and the desired analyteconcentration value or range of values may be further based on thedifficulty level. The evaluated user data may include retrospectiveanalyte concentration data for retrospective meal data.

The displaying an output may include displaying an indicator of thedetermined pattern. The determined pattern may be selected from thegroup consisting of: overnight lows, postprandial spikes, a type ofdiscriminated fault, a pattern of high analyte variability, or aconsistent pattern of weekly highs or lows. The method may furtherinclude determining a baseline analyte concentration pattern for theuser, and the determined pattern may be a consistent variation from thebaseline pattern. The evaluating user data to determine a pattern stepmay further include initiating a discovery mode, where in the discoverymode, one or more questions are posed on the user interface, userresponses to the one or more questions constituting additional userdata, and the evaluating user data step may further include evaluatingthe additional user data along with the monitored and stored analyteconcentration data to determine a pattern. The additional user data maybe meal data or activity data. The method may further includetransmitting the output to a cloud connected entity.

In further aspects and embodiments, the above method features of thevarious aspects are formulated in terms of a system as in variousaspects. Any of the features of an embodiment of any of the aspects,including but not limited to any embodiments of any of the first throughfourteenth aspects referred to above, is applicable to all other aspectsand embodiments identified herein, including but not limited to anyembodiments of any of the first through fourteenth aspects referred toabove. Moreover, any of the features of an embodiment of the variousaspects, including but not limited to any embodiments of any of thefirst through fourteenth aspects referred to above, is independentlycombinable, partly or wholly with other embodiments described herein inany way, e.g., one, two, or three or more embodiments may be combinablein whole or in part. Further, any of the features of an embodiment ofthe various aspects, including but not limited to any embodiments of anyof the first through fourteenth aspects referred to above, may be madeoptional to other aspects or embodiments. Any aspect or embodiment of amethod can be performed by a system or apparatus of another aspect orembodiment, and any aspect or embodiment of a system or apparatus can beconfigured to perform a method of another aspect or embodiment,including but not limited to any embodiments of any of the first throughfourteenth aspects referred to above.

Advantages of one or more Implementations may include one or more of thefollowing. Users may be enabled to learn how to better their healththrough the use of programs, both self-selected and system-determined onthe basis of user data. Users may further be enabled to optimize sportsand weight loss efforts through the use of the described principles.Other advantages will be understood from the description that follows,including the figures and claims.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments now will be discussed in detail with an emphasison highlighting the advantageous features. These embodiments depict thenovel and non-obvious systems and methods according to presentprinciples, for use in educating users and other purposes, shown in theaccompanying drawings, which are for illustrative purposes only. Thesedrawings include the following figures, in which like numerals indicatelike parts:

FIG. 1 is a flowchart according to present principles.

FIG. 2 is a schematic of an exemplary system according to presentprinciples.

FIG. 3 illustrates various exemplary types of displayed outputs.

FIG. 4 illustrates an output in which an analyte trace is plotted alongwith an ideal or modeled analyte response.

FIG. 5 illustrates a displayed output in which an analyte trace isplotted along with an envelope of an ideal or modeled analyte response.

FIG. 6 illustrates an exemplary user interface in which a combination oftypes of outputs is displayed. It will be understood that combinationsof other outputs are encompassed by present principles (in this and inother figures).

FIG. 7 is an exemplary user interface in which a combination of types ofoutputs is displayed.

FIG. 8 is another exemplary user interface in which a combination oftypes of outputs is displayed.

FIG. 9 is an exemplary user interface in which various ranges offat/carbohydrate burn rates are displayed.

FIG. 10 is a user interface of a watch, illustrating various types ofoutputs.

FIG. 11 is an exemplary user interface in which various ranges ofglucose concentration level are displayed, along with other performanceparameters.

FIG. 12 is an exemplary user interface in which various ranges ofglycemia are displayed.

FIG. 13 is a more abbreviated exemplary user interface in which variousranges of glycemia are displayed.

FIG. 14 is another more abbreviated exemplary user interface in whichvarious ranges of glycemia are displayed.

FIG. 15 is an exemplary user interface in which various ranges aredisplayed, in a tachometer format.

FIG. 16 is an exemplary user interface in which various ranges offat/carbohydrate burn rates are displayed, in a tachometer format.

FIG. 17 shows an exemplary difference plot against time.

FIG. 18 shows an exemplary user interface in which glucoseconcentrations are plotted against time.

FIG. 19 is a flowchart indicating a method according to presentprinciples.

FIG. 20 shows an exemplary glucose plot against time.

FIG. 21 shows an exemplary user interface in which meals are plottedagainst time, and with respect to glucose levels.

FIG. 22 shows an exemplary user interface in the form of a food pyramid.

FIG. 23 shows an exemplary user interface in which meals are displayedas a function of their glycemic effect on a user.

FIG. 24 shows an exemplary user interface simplified for type II users.

FIG. 25 shows another exemplary user interface simplified for type IIusers.

FIGS. 26A-26F illustrate other exemplary user interfaces, particularlysuitable for type II users.

FIG. 27 shows an exemplary user interface simplified for type II users.

FIGS. 28A-28H illustrate exemplary elements which may be advantageouslyrendered on user interfaces, particularly suitable for type II users.

FIGS. 29A-29C illustrate other exemplary user interfaces, particularlysuitable for type II users.

FIGS. 30A-30C illustrate other exemplary user interfaces, particularlysuitable for type II users.

FIGS. 31A-31C illustrate other exemplary user interfaces, particularlysuitable for type II users.

FIGS. 32A-32C illustrate other exemplary user interfaces, particularlysuitable for type II users.

FIGS. 33A-33D illustrate other exemplary user interfaces, particularlysuitable for type II users.

FIG. 34 illustrates an exemplary user interface, particularly suitablefor type II users.

FIG. 35 illustrates the user interface of FIG. 34, in which an event hasbeen added.

FIG. 36 illustrates an exemplary implementation of a notification on ahome screen of a smart phone.

FIG. 37 is a more detailed flowchart indicating an application of theflowchart of FIG. 1, specific to programmatic learning and discovery,and in particular to assist diabetes and pre-diabetes patients inlearning how to better manage their health.

FIG. 38 is an application of the method of FIG. 37, in which a specificprogram has been suggested for the user.

FIG. 39 is an application of the method of FIG. 37, in which a user hasselected a specific subprogram.

FIG. 40 is another more detailed flowchart indicating an application ofthe flowchart of FIG. 1, specific to steps following the determinationof a pattern within data, e.g., glucose data, and in particular toassist diabetes and pre-diabetes patients in learning how to bettermanage their health.

FIG. 41 is another more detailed flowchart indicating an application ofthe flowchart of FIG. 1, specific to determining an optimal medicamentregimen.

FIG. 42 is another more detailed flowchart indicating an application ofthe flowchart of FIG. 1, specific to determining an optimal basalinsulin regimen.

FIG. 43 is a flowchart illustrating an implementation of a method fordetermining a drug regimen.

FIG. 44 is a flowchart illustrating an implementation of a method fordetermining a baseline and characteristic signatures for use in themethod of FIG. 43.

FIG. 45 is another more detailed flowchart indicating an application ofthe flowchart of FIG. 1, specific to determining optimal parameters forsports, health, or fitness.

FIG. 46 is a schematic view of a device which may be employed for sportsor weight loss optimization.

FIG. 47 is another more detailed flowchart related to sportsoptimization, and in particular for the monitoring of lactate or otheranalytes.

FIG. 48 is a graph showing a training-induced right shift in lactatelevels.

FIG. 49 illustrates aerobic and lactate thresholds.

FIG. 50 illustrates a crossover in fat and carbohydrate consumption as afunction of aerobic power.

FIG. 51 is another more detailed flowchart indicating an application ofthe flowchart of FIG. 1, specific to determining optimal parameters forweight loss.

FIG. 52 illustrates a more detailed flowchart corresponding to theflowchart of FIG. 51, in particular showing exemplary types of datawhich may be tracked.

FIG. 53 is a schematic view of a device which may be employed for sportsor weight loss optimization, the device including a lactate sensor.

FIG. 54 is a view of an exemplary embodiment of a continuous analytesensor.

FIGS. 55-57 are other views of the sensor of FIG. 54, illustratingvarious embodiments of a sensor system.

FIG. 58 illustrates one embodiment of a sensor system in which aplurality of sensor devices are grouped together to form a sensor array.

Like reference numerals refer to like elements throughout. Elements arenot to scale unless otherwise noted.

DETAILED DESCRIPTION

Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to a substance or chemical constituent in abiological fluid (for example, blood, interstitial fluid, cerebralspinal fluid, lymph fluid or urine) that can be analyzed. Analytes caninclude naturally occurring substances, artificial substances,metabolites, and/or reaction products. In some embodiments, the analytefor measurement by the sensor heads, devices, and methods is glucose.However, other analytes are contemplated as well, including but notlimited to acarboxyprothrombin; acylcarnitine; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acidprofiles (arginine (Krebs cycle)), histidine/urocanic acid,homocysteine, phenylalanine/tyrosine, tryptophan); andrenostenedione;antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine);biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4;ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol;cholinesterase; conjugated 1-B hydroxy-cholic acid; cortisol; creatinekinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine;de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylatorpolymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cysticfibrosis, Duchenne/Becker muscular dystrophy, analyte-6-phosphatedehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D,hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis Bvirus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD,RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol);desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanusantitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D;fatty acids/acylglycines; free B-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, B);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The term “calibration” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the process of determiningthe relationship between sensor data and corresponding reference data,which can be used to convert sensor data into meaningful valuessubstantially equivalent to the reference data, with or withoututilizing reference data in real time. In some embodiments, namely, incontinuous analyte sensors, calibration can be updated or recalibrated(at the factory, in real time and/or retrospectively) over time aschanges in the relationship between the sensor data and reference dataoccur, for example, due to changes in sensitivity, baseline, transport,metabolism, and the like.

The terms “calibrated data” and “calibrated data stream” as used hereinare broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and are not to belimited to a special or customized meaning), and refer withoutlimitation to data that has been transformed from its raw state (e.g.,digital or analog) to another state using a function, for example aconversion function, to provide a meaningful value to a user.

The term “algorithm” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to a computational process (for example,programs) involved in transforming information from one state toanother, for example, by using computer processing. In many cases suchan algorithm serves to transform a computing environment into a specialpurpose computing environment to solve one or more technologicalchallenges.

The term “counts” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to a unit of measurement of a digital signal.In one example, a raw data stream (or just “raw” data) measured incounts is directly related to a voltage (e.g., converted by an A/Dconverter), which is directly related to current from the workingelectrode.

The term “sensor” as used herein is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to the component or region of a device bywhich an analyte can be quantified.

The term “glucose sensor” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to any mechanism (e.g.,enzymatic or non-enzymatic) by which glucose can be quantified. Forexample, some embodiments utilize a membrane that contains glucoseoxidase that catalyzes the conversion of oxygen and glucose to hydrogenperoxide and gluconate, as illustrated by the following chemicalreaction: Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used herein arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to one ormore components being linked to another component(s) in a manner thatallows transmission of signals between the components. For example, oneor more electrodes can be used to detect the amount of glucose in asample and convert that information into a signal, e.g., an electricalor electromagnetic signal; the signal can then be transmitted to anelectronic circuit. In this case, the electrode is “operably linked” tothe electronic circuitry. These terms are broad enough to includewireless connectivity.

The term “in vivo portion” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the portion of the device(for example, a sensor) adapted for insertion into and/or existencewithin a living body of a host. Conversely, “ex vivo portion” relates toa portion outside the living body.

The term “variation” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to a divergence or amount of change from apoint, line, or set of data. In one embodiment, estimated analyte valuescan have a variation including a range of values outside of theestimated analyte values that represent a range of possibilities basedon known physiological patterns, for example.

The terms “physiological parameters” and “physiological boundaries” asused herein are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and refer withoutlimitation to the parameters obtained from continuous studies ofphysiological data in humans and/or animals. For example, a maximalsustained rate of change of glucose in humans of about 4 to 5 mg/dL/minand a maximum acceleration of the rate of change of about 0.1 to 0.2mg/dL/min² are deemed physiologically feasible limits; values outside ofthese limits would be considered non-physiological. As another example,the rate of change of glucose is lowest at the maxima and minima of thedaily glucose range, which are the areas of greatest risk in patienttreatment, thus a physiologically feasible rate of change can be set atthe maxima and minima based on continuous studies of glucose data. As afurther example, it has been observed that the best solution for theshape of the curve at any point along a glucose signal data stream overa certain time period (for example, about 20 to 30 minutes) is astraight line, which can be used to set physiological limits. Theseterms are broad enough to include physiological parameters for anyanalyte.

The term “measured analyte values” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to an analyte valueor set of analyte values for a time period for which analyte data hasbeen measured by an analyte sensor. The term is broad enough to includedata from the analyte sensor before or after data processing in thesensor and/or receiver (for example, data smoothing, calibration, andthe like).

The term “estimated analyte values” as used herein is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to an analyte valueor set of analyte values, which have been algorithmically extrapolatedfrom measured analyte values.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

The phrase “continuous glucose sensor” as used herein is a broad term,and is to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to a device thatcontinuously or continually measures the glucose concentration of abodily fluid (e.g., blood, plasma, interstitial fluid and the like), forexample, at time intervals ranging from fractions of a second up to, forexample, 1, 2, or 5 minutes, or longer.

The phrases “continuous glucose sensing” or “continuous glucosemonitoring” as used herein are broad terms, and are to be given theirordinary and customary meaning to a person of ordinary skill in the art(and are not to be limited to a special or customized meaning), andrefer without limitation to the period in which monitoring of theglucose concentration of a host's bodily fluid (e.g., blood, serum,plasma, extracellular fluid, tears etc.) is continuously or continuallyperformed, for example, at time intervals ranging from fractions of asecond up to, for example, 1, 2, or 5 minutes, or longer. In oneexemplary embodiment, the glucose concentration of a host'sextracellular fluid is measured every 1, 2, 5, 10, 20, 30, 40, 50 or 60seconds.

The term “substantially” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to being largely but notnecessarily wholly that which is specified, which may include an amountgreater than 50 percent, an amount greater than 60 percent, an amountgreater than 70 percent, an amount greater than 80 percent, an amountgreater than 90 percent, or more.

The terms “processor” and “processor module” as used herein are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to acomputer system, state machine, processor, or the like, designed toperform arithmetic or logic operations using logic circuitry thatresponds to and processes the basic instructions that drive a computer.In some embodiments, the terms can include ROM and/or RAM associatedtherewith.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of another analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, non-invasive, intraocularand/or intravascular (e.g., intravenous) device. In some embodiments,the device can analyze a plurality of intermittent blood samples. Theglucose sensor can use any method of glucose measurement, includingenzymatic, chemical, physical, electrochemical, optical, optochemical,fluorescence-based, spectrophotometric, spectroscopic (e.g., opticalabsorption spectroscopy, Raman spectroscopy, etc.), polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (e.g., doctor), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor capable of measuring the concentration of glucose in a host, thesystems and methods of embodiments can be applied to any measurableanalyte. Some exemplary embodiments described below utilize animplantable glucose sensor. However, it should be understood that thedevices and methods described herein can be applied to any devicecapable of detecting a concentration of analyte and providing an outputsignal that represents the concentration of the analyte.

In some embodiments, the analyte sensor is an implantable glucosesensor, such as described with reference to U.S. Pat. No. 6,001,067 andU.S. Patent Publication No. 2011/0027127-A1. In some embodiments, theanalyte sensor is a transcutaneous glucose sensor, such as describedwith reference to U.S. Patent Publication No. 2006/0020187-A1. In yetother embodiments, the analyte sensor is a dual electrode analytesensor, such as described with reference to U.S. Patent Publication No.2009/0137887-A1. In still other embodiments, the sensor is configured tobe implanted in a host vessel or extracorporeally, such as is describedin U.S. Patent Publication No. 2007/0027385-A1. These patents andpublications are incorporated herein by reference in their entirety.

The following description and examples describe the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

FIG. 1 is a flowchart 10 illustrating a basic method according topresent principles. The basic method of flowchart 10 is exemplified andapplied below in the specification, and in particular within subsequentflowcharts of FIGS. 37, 38, 39, 3, 41, 42, 45, 47, 51, and 52. In thisbasic method, a perturbation is applied to a biological system (step11). An analyte level is tracked corresponding to the perturbation (step12). The analyte level is then evaluated, and in particular with respectto the perturbation (step 14). An output is then displayed responsive tothe evaluation (step 16). Various types of outputs will be understoodand are described below.

In more detail, and as will be described below in greater detail withrespect to other flowcharts, the perturbation to a biological systemwill often include a program followed by a user in the treatment of adisease, e.g., diabetes, but may also be employed to prevent (orreverse) such diseases, e.g., when a user is prediabetic ornon-diabetic. Systems and methods according to present principles arenot limited to predetermined programs, however. For example, a user maystart wearing the sensors described here, and the systems and methodsmay learn from the user's responses, and from such learning a program orsuggested perturbation may be suggested. Alternatively, the systems andmethods may recommend other actions, or simply give feedback. In suchimplementations, by having the system and method learn by user data,whether user entered or sensor measured, the system and method configurea computing environment such as an analyte monitor to operate better,faster, and more efficiently, as less computing cycles are required toprovide the same (or better) output to a user. Applications of thesystems and methods may include sports and health optimization, as wellas weight loss and other areas.

In these applications, various analyte levels may be tracked in thesecond step above (step 12), and commonly glucose will be one suchanalyte. Other analytes will also be understood and are described below.For example, lactate may be tracked, as well as insulin and/ortriglycerides. The program may further include determining a program fora user to follow, e.g., a meal program or an exercise program, andtracking an analyte level over a period of time, e.g., a week, to seehow the analyte level, and thus the user's health, is affected by theprescribed program. Outputs may be provided to provide comment on theevaluation, to provide numerous types of evaluative outputs, to suggestother programs for the user to try, and so on.

The perturbation, as noted, is often a program to try which provides aperturbation or change to a user's “normal” routine. By seeing how thechange to the user's routine affects the analyte level or other aspectsof a patient's health, conclusions may be drawn as to the usefulness ofthe change to the user, and such conclusions can be manifested withinthe various outputs. As such, the perturbation can be initiated by theuser who interacts with the computing environment, the computingenvironment in signal communication with the analyte sensor andreceiving data therefrom, and in this way the user can initiate aprogram to learn how to better their health. For example, a user maydesire to learn more about the effect of food on their health.Alternatively, the user may desire to learn more about the effect ofexercise on their health.

In the above systems, perturbations are initiated by the user. However,in some cases, the system including an appropriate computing environmentcan detect a pattern in a physiological parameter of a patient, e.g.,their glucose level, and as a result the system may prompt the user totry a system-generated program, i.e., to try a perturbation (program) soas to affect the detected pattern or its consequences. As a particularexample, the system may detect a pattern of overnight lows (in glucoselevel), and may suggest a program for a user to try to treat the same.

By the suggestion and implementation of a program to perturbation,systems and methods according to present principles are configured toboth (a) turn the computing environment of a continuous analyte monitorinto a special purpose computing environment configured for userprogrammatic learning, and (b) allow the computing environment toautomatically learn (or prepare to learn), in an efficient way, userparameters, thus freeing the user (and a HCP) from having to enter anditerate suggested parameters in an ad hoc and haphazard manner.

In some cases, even without the detection of a pattern, the systems andmethods according to present principles may suggest programs for theuser to try, either based on a past history of the patient or startingwith a default program. With respect to either of these, the system maymake a determination as to a suitable program on which to start theuser. In this way, even a neophyte user with no knowledge of the system,and little knowledge of their own biological response to meal andexercise routines, may be enabled to start a program. In some cases, thesystems and methods may use retrospective user information and in someimplementations contemporaneous user input so as to select potentialprograms for a user to try. Alternatively, one or more default programsmay be provided which the system displays to users as potential startingpoints. In any case, these methods provide significant technologicaladvantages over prior systems in which no programs were provided to auser, much less personalized and customized programs.

As an example of the use of retrospective data, if previously measureduser data indicates the patient is prone to overnight lows in glucoseconcentration level, even if the same do not constitute a pattern perse, the systems and methods may suggest altering the evening meal andevaluating the effect of different evening meals on the occurrence ofovernight lows. In the same way, the systems and methods may suggest aprogram for exercising during the day, instead of at night, to achievethe same purpose. Other variations will also be understood. Using suchfacilities, the user is relieved from having to hypothesize programs ontheir own.

As an example of the use of default programs, systems and methods may be“preloaded” with default program options, particularly for the casewhere the system, using retrospective analysis, can discern noparticular single unambiguous program to suggest, or where no suchsuggestion algorithm is in place. In this case, the system might providea default option of varying a single meal for a weeklong predeterminedperiod, or proposing an exercise routine to be carried out three timesper week.

In certain implementations according to present principles, the systemsand methods may start sua sponte in the determination of data andinformation required for the performance of the flowchart 10. In otherimplementations, the systems and methods may include analysis of userdata. For example, the determinations of detected patterns may includeanalysis of retrospective glucose data over time to determine a userprofile. In yet other implementations, the systems and methods mayinitiate by beginning a period of measurement in which patterns aredetermined corresponding to a baseline glucose profile. In the latter, auser may be instructed to conduct themselves according to their normalhabits, eating their typical meals and exercising with their typicalfrequency and intensity. The systems and methods can define such as abaseline profile, and may construct the same from patterns usingappropriate pattern recognition software. Then, the systems and methodsmay detect patterns or events representing deviations from the same insubsequent periods of measurement. For example, from the baselinepattern, the system may detect a pattern of overnight lows. However, ifthe user starts having hyperglycemic excursions on, e.g., Sundayafternoons, either as measured sua sponte or as measured with respect tothe measured baseline profile, then the system may start to detect anunhealthy pattern as may be caused by, e.g., watching football andconsuming unhealthy snacks. The same may then be used as a pattern toaddress with a program.

However the program is determined, use of the program leads to eventualmore efficient processing within the computing environment of theanalyte monitor, as problematic patterns and events are minimized orreduced in frequency.

The tracking of analyte levels (step 12) may encompass the tracking ofone analyte level or multiple analyte levels. In some cases, multipleanalyte levels may be measured with a single sensor assembly, althoughcommonly distinct sensor technologies are employed, e.g., using anotheranalyte sensor/transmitter, or different portions of a single sensorassembly. That is, multi-analyte sensors may be co-located or usesimilar sensing technologies, e.g., from a single manufacturer, or thesame may be embodied by discrete and separate sensing technologies fromdifferent manufacturers, where the data is aggregated in a singlesource, e.g., a smart phone or other electronic device.

Where analyte levels are provided by signals measured by a sensor, thesame are often entered into a monitoring device such as a smart phone orother mobile device running monitoring applications, including, e.g.,dedicated CGM devices. However, in some implementations, where sensorsare not signally coupled to the monitoring device, a user may enter ananalyte value himself or herself, e.g., after reading the same from asensor display.

Exemplary types of analytes or other physiological data which may bemonitored include glucose, ketones, lactic acid, lactate, glycerol,insulin, VCO2, VCO, free fatty acids, cortisol, testosterone,biochemical indicators of mental health, body temperature, blood urea,nitrogen, bicarbonate, oxidants, oxidizing species, and the like.Exemplary analytes will also be described below in connection withrespective applications. Other types of biological measurements may alsobe employed, including measurements of metabolism by a sensor 34, andother physiological quantities employed may not necessarily involvemeasurement of an analyte.

In some cases, and referring to the system 20 of FIG. 2, a monitoringdevice 21 may receive data from a sensor and transmitter 22 and may beconnected via a local network hotspot 25 (or using a cellular network)to a network or other cloud-based source of data 24. The monitoringdevice is often a mobile device 21, but the same may also be a dedicateddevice such as a CGM receiver. In this case the source of data 24 aswell as the sensor/transmitter 22 may provide data as needed by thedescribed methods. In some cases the monitoring device 21 will performthe steps, and in such cases it may receive the data or may receive acopy of the data. In other cases an external computing environment 28(within the cloud) or 32 (within the local network) may performcalculations and thus may receive the data from the source of data 24 orthe sensor/transmitter 22 (in this case the monitoring device 21provides the data from the sensor to the external computing environment28 or 32). The monitoring device 21 may further include non-transitorycomputer readable memory running an application or “app” 27. The app 27may include functionality for receiving data from sensors 22 and 22′ orthe like, as well as analyzing such and other data to perform themethods described herein with respect to flowcharts 37, 38, 39, 3, 41,42, 45, 47, 51, and 52.

As will be described in greater detail below, the cloud may sourcesignificant amounts of data, and in such cases the monitoring device 21may receive the same using the configuration of FIG. 2.

Returning to the tracking of analyte levels (step 12) of FIG. 1, evenwhere just one analyte level is monitored, other associated data may beaccumulated and stored. For example, besides an analyte level value,generally an analyte concentration value and a timestamp of the analytedata point may be returned. From such data, the frequency of analytedata points may be calculated, as well as the frequency of how often theuser checks the analyte data, e.g., via screen refreshes, user's screenelement activations, or the like, calibration values, the timestamps ofcalibration values, the frequency of calibrations, and so on. Theanalysis of an analyte data level over time can lead to additional dataas well, e.g., time rates of change of the analyte level, e.g.,velocities, accelerations, and so on.

Data about one or more analyte levels may be accompanied by other datauseful in the evaluation step (step 14). Such data may include activitydata, including data about the intensity and duration of activity.Exemplary such activity data include exercise data, although otheractivity may also be measured and quantified. For example, activity datamay be stratified into multiple levels including: heavy exercise, mediumexercise, light exercise, a sedentary state, and a sleeping state.

Activity may be measured in various ways, particularly by use of anaccelerometer 24 within the mobile device 21 (see again FIG. 2).External accelerometers may also be employed, e.g., as may be availablewithin a running watch or the like. In some cases, an accelerometer maybe built into the sensor or sensor electronics including transmitter. Insome cases, e.g., where a user is exercising by running or jogging, GPSdata may also be determined and used. GPS data may also be employed todetermine a velocity of the user, and may be able in that way todetermine an intensity of the running or jogging. Other activitymonitors may include, e.g., heart rate monitors, pulse meters, and soon. Activity may also be directly entered by a user, e.g., by directdata entry 29 on a user interface 26, such as “I ran 3 miles”. In anindirect user entry technique, the use of hash tags 36 may be employedto allow convenient entry by a user of common entries. For example, if auser eats the same size and type of cereal every day, entering “#cereal”may input data of a datatype “food” and may also automatically enter aglycemic index and/or a calorie count appropriate to the bowl of cereal.In the same way, entering “#3miles” may enter data of a datatype“activity:exercise” and with a value suitable to 3 miles of running. Thetiming of the entry may automatically timestamp the occurrence of theevent, e.g., eating a bowl of cereal or running 3 miles, if the hash tagis entered at substantially the same time as the event.

Other aspects of sizes and types may also be entered. For example, theuser may enter “I ate a bowl of cereal” but may also enter “I ate a1-cup bowl of brand X cereal”. Other types of data which may be enteredby a user can include a report of best days, e.g., least time inhypoglycemia, worst days, e.g., most time in hypoglycemia, best meals,or the like. Such data can also be sourced from the system and reportedas part of the output.

The output of the system may be tied to the evaluation and thus to thedata entered. Accordingly, if data such as “I ate a bowl of cereal” isentered as a text string, the same may be evaluated with the resultingglucose (or other analyte) trace. In this case, one exemplary output maybe to display the effect of that day's meal, i.e., cereal, via a glucosetrace, with a textual legend or other indicator associated with theentry, e.g., “result of a ‘bowl of cereal’. However, the data entry mayalso be more quantified, in which case the glucose trace can be shownwith a numerical or graphical indicator of the meal, rather than just atext string. For example, if it is known that the bowl of cereal inquestion has a certain number of calories, or a certain glycemic index,such may be displayed as a more accurate indicator in lieu of the textstring. The text string may be symbolized in a convenient way, allowingthe user a more “ready” reference of the cause of the glucose trace.Such symbolizations may include photographs taken of the food at thetime of eating, e.g., which may be associated via an appropriate mobiledevice application with the entry about the food. In this regard it isnoted that in some cases applications may be employed which, using aphotograph of food, can estimate or otherwise approximate a number ofcalories of pictured food or a glycemic index thereof. In this case,additional user entry may not be needed beyond the capture of thephotograph. The use of such pictorial representations is described ingreater detail below with respect to displayed outputs.

In this regard it is noted that users commonly eat similar meals overtime. For example, a user may commonly eat a particular type of fastfood hamburger several times a week. By creating a hash tag or otherentry element which is convenient to enter, and which can be quantifiedby generally known information about fast food nutritional statistics,the user may be provided with a convenient way to enter meal data ofhigh accuracy.

Systems and methods according the present principles may also deducemeal data, given location data. For example, a method may be employed ofdetermining meal information using location data, including determiningone or more meal sources, e.g., restaurants, associated with thelocation, and further determining one or more likely meal choices givena user's meal history associated with the location. The system caneither posit a meal, given the precise GPS location, and prompt the userfor confirmation, or can present the user with various meal options andhave the user select which is the current meal choice. In one example,the computing environment being provided with GPS data may be enough todeduce meal information. For example, if a particular fast foodrestaurant is at a particular GPS location, and the user is detected tobe at that GPS location, then meal data may be deduced according to theuser's usual menu choice, e.g., subject to user confirmation.Alternatively, if the user sometimes orders different options, the usermay be presented on the user interface 26 of the mobile device with thevarious options, and the user may conveniently choose which option theyordered that day. A default choice may be selected if it is the user'spredominant choice or is, e.g., a common choice for the user on thatparticular day of the week. Such a user interface facility may allow thecomputing environment of the mobile device 21 to operate in a moreefficient manner, as accurate data is immediately obtained, e.g., via alookup table associated with the hash tag, without the need forsignificant user interaction with the device to obtain an accuraterecording of the food eaten. Alternatively, purchase information couldbe employed to determine such information. For example, if a user eatsat a restaurant and pays for the meal with an online system, e.g.,ApplePay, the system can use data about the transaction to deduce mealinformation. In some implementations, the meal information may bepopulated using the electronic transaction data and offered as a choicefor the user to confirm that the same is correct and intended toconstitute meal data. Using systems and methods according to presentprinciples, implementation of the above options allows the entry of datain a way that is more accurate than prior attempts, leading to thetransformation of data (via algorithmic processing) being performed in amore accurate way, which in turn leads to more accurate outputs.

Various other data can be sourced from cloud-based or other networksources. Such may include data noted above as well. For example, in somecases an analyte sensor might send measurement data to a cloud-basedsource, which is only later pulled down to a mobile device 21. In thiscase, even analyte data may be sourced from the cloud.

Other cloud or network data which may be employed in evaluation stepsmay include demographic and other such data. In this usage “other data”may be broadly construed, and may include data about the proximity of auser to a clinic or hospital, the user's ability to transport themselvesor to use public transportation, user health data, and the like.

Other significant types of cloud or network data include data aboutother users, particularly other users with similar demographics to asubject user. By consideration of such users with similar demographics,and in particular aspects about such users' responses to perturbations,additional data about the subject user may be inferred, deduced, orotherwise gleaned. In particular, such additional information allowstailoring or personalization of suggested or proposed programs to thesubject user, as it may be expected that if users have similar profiles,similar programs might be appropriate for them. This type ofpersonalization significantly enhances the efficiency of the underlyingcomputer environment, as the same is enabled to undergo far fewercomputations in order to arrive at a suitable therapy for the subjectuser.

Returning to FIG. 1, the evaluation of the analyte level (and otheroptional data) (step 14) is generally made against the perturbation 10.In some cases the evaluation may be preceded by an optional step ofanalyzing the signal corresponding to the analyte level (or analyzingother received data). Such may include the derivation of associateddata, e.g., time rates of change, signal analysis, or performing otherprocessing on the analyte signal trace.

Step 14 provides an evaluation of the analyte level and other optionaldata against the perturbation. More particularly, data about theanalyte, which may include values, time rates of change, or the like, aswell as optionally other data, is evaluated against the perturbation.For example, where the perturbation is a program being followed by thebiological system, i.e., a user, then the evaluation is some associationof the response of the biological system, e.g., analyte levels, as aresult of the program.

While specific outputs are discussed below, here it is noted that theresponse of the biological system, as measured by analyte levels andother data, may take several forms. These are generally discussed belowin order of increasing sophistication, and subsequent levels generallybuild on information and data received or determined in lower levels.For example, a deduction or inference may build on a correlation.

In more detail, in one form, the evaluation may be to evaluate the dataagainst time, in this case two sets of superposed data (step 13). Inother words, to show an indication of the program as well as anindication of the analyte level or other data. In many cases theseindications will be shown with respect to a timeline, as each representsevents occurring in time, and generally the analyte level will have somerelationship to the program. Showing both with respect to a time axismay bring out this relationship, even if, as is commonly seen, theanalyte trace is time-shifted with respect to program events. Forexample, if the program includes varying the type of breakfast consumedby the user, this type of evaluation would be to receive and store bothsets of data, i.e., food data and analyte level data, as a function oftime, over a common time period.

In a more complex evaluation, a correlation may be evaluated between thetwo sets of data (step 15), and the correlation may then be displayed orindicated in the output step. In this case the evaluation step mayinclude not only plotting the data against time but also determiningthat a relationship exists and what the relationship consists of. In theabove example, this type of evaluation may include evaluating arelationship between food consumed and the analyte level data, e.g.,that different foods affect the analyte level differently. In some casesthe evaluated relationship results in the obtaining or determination ofinformation indicative of, or insights about, the effect of one set ofdata on the other. The determination of the relationship leads tosubsequently more efficient processing as the relationship allows theuse of deductions and inferences which are more efficient for acomputing environment to calculate than the case where no relationshipis used.

In a still more complex evaluation, a deduction or inference may be made(step 17), at least about an underlying cause of the relationship notedin step 15. In this step, the deduction or inference evaluated may thenprovide (in the output step) additional information to the user aboutthe underlying cause of the relationship. In the above example, theevaluation, which can be displayed to the user in a number of forms, maybe that high glycemic index breakfast food items cause the analyte levelto significantly increase. As with the relationship data, the evaluatedcause may result in the obtaining or determination of informationindicative of, or insights about, the effect of one set of data on theother or a cause related to each.

In a still more complex evaluation, but which may again build on priorevaluation steps, the evaluation may include suggesting a lifestylemodification (step 19). In this step, the cause is addressed and asuggestion is made to the user, and in most cases the suggestion isactionable, i.e., provides an act for the user to perform to bettertheir health. The lifestyle modification may be determined on a basis ofa lookup table tied to various causes, but may also be more personalizedto the user based on retrospective user data as well as, in some cases,cloud sourced data. In some cases the lifestyle modification is a newperturbation (step 33), i.e., a new program for the user to follow, andin this case the process may simply repeat. Importantly, the newperturbation may be based on the suggested lifestyle modification ofstep 19, the relationship of step 17, the correlation of step 15, or thedata of step 13, or combinations of the above. The new perturbation mayfurther be based on retrospective or other prior user data, as well ascloud-based data. By doing so, systems and methods according to presentprinciples can more efficiently converge on a solution (modification),thus saving computing cycles, battery power, and so on. In someimplementations, the new perturbation may be that the same program isfollowed but with a lower tolerance for deviations, e.g., to challengethe user to follow a tighter analyte response envelope. For example,adaptive target glucose levels may be set for patients. The targetlevels that would display and potentially alert would be based on theprevious history and the goals the patient was trying to achieve, e.g.,as part of the program. Rather than setting a static level for a goal,the same may dynamically change to reflect improvements and changes to atarget healthy level, e.g., may require the user to perform greateranalyte control if the user has indicated, e.g., by their success, theability to do so.

By tying one or more types of data to a program, performing a suitableevaluation, and providing the user with an informative output, thecomputing environment performing these steps operates in a moreefficient manner as the computing environment is able to “home in on” asolution for a healthier lifestyle for a user in a rapid manner,eliminating programs not of use to the user and providing the user withactionable information in an efficient manner.

Various types of outputs are now described. It will be understood thatwhile the evaluations above are described in terms of analyte and otherdata sets, the results of the evaluations, and in particular the outputspertaining thereto, may be in a number of forms based on the data, butnot necessarily data traces themselves. In particular, the outputs maybe specifically geared to, in the case of diabetes,non-insulin-dependent type II users, or newly-diagnosed type I users,who may not have as much experience receiving and analyzing glucoselevels as their type I and type II insulin-dependent counterparts. Inthis sense, outputs will generally provide overall or summary data, asopposed to providing so much data that the user becomes overwhelmed. Forexample, data may be provided in such a way so as to indicatedifferences, e.g., how far from the norm the user is, including in aqualitative way, as the user may not be familiar with absolute values ordesired ranges. In an implementation of this concept, the display mayshow a trend graph (see FIG. 17, illustrating a plot 192 with adifference curve 194 as a function of time) that is the differencebetween the actual values from that expected from a healthy individual.For example, if “healthy” is defined as 70 and 120 mg/dL, then the plotwould show the difference between the actual and the average, e.g., 90mg/dL. The definition of “healthy” could also change if the systemdetects a glucose peak during lunch, e.g., if a user is often running“high”, then systems and methods according to present principles mayindicate a worse result than if the user is only temporarily showing ahigh number. Such systems may be generated from simple algorithms orfrom more complex ones, employing retrospective information to generatethe plotted values.

In a related implementation, where exemplary and/or average glucosevalues are portrayed to the user, e.g., to convey to the user the effectof a meal (which may also be portrayed against a desired or idealglucose trace), a glucose concentration value may be illustrated at thestart of the meal as if the user was starting from a zero glucoseconcentration. Such may significantly clarify the presentation of dataon a graph, and thus convey the meaning of the glucose trace moreclearly to an inexperienced user. For example, referring to FIG. 18, agraph 196 is shown in which an actual glucose concentration trace 202 isplotted superposed on an ideal glucose trace 198, an event, indicated bythe traces, being consumption of a meal. By subtracting out the glucoseconcentration value at the beginning of the meal, e.g., by starting bothtraces from zero, as illustrated by the glucose trace 206 plotted withthe superposed ideal glucose trace 204, the effect of the meal may bemore easily seen by the user. In this case, the meal trace follows theideal trace well.

For portrayal as part of a program, such traces may indicate averagevalues, and the averages may be taken over various time periods, e.g.,1, 3, 6, 12, 24 hours, and so on. In other cases, glucose traces may bedisplayed to the user as example depictions of glucose events occurringwithin or as a result of the program or other perturbation.

As examples of qualitative ways of providing data, colors or the use ofrange indicators (in lieu of value indicators) may be employed toprovide information to a user. Other examples include the use of rangesor zones, textual verbiage, actions or comments by avatars, and thelike, as will be described below.

The displayed outputs may thus be simple, intuitive, and easy tounderstand, e.g., the outputs may be configured such that it is easy tounderstand what type of action is being requested of the user, and maybe easily relatable to everyday life. In one implementation,“low-resolution” data may be initially provided to the user, e.g., acolor indicating whether a goal associated with the program has beenmet, or showing, e.g., the impact of exercise on a glucose level.” Highresolution” data may be provided in the background, e.g., available tothe user via a swiping action. In some cases the low resolution data maybe action based, instead of a color or value, providing a comment or anaction performable by the user. High-resolution data may also beprovided to the cloud for subsequent use by a healthcare practitioner(“HCP”) or other users.

In more detail, referring to the diagram 30 of FIG. 3, and as oneexample of an output, systems and methods according to presentprinciples may provide a new perturbation, e.g., a new program for theuser to follow, or a selection of several such potential or proposedprograms for a user to select (step 38). The new perturbation maygenerally be at least partially based on the evaluation step notedabove. For example, a new program according to step 38 may include achange to a prior regimen, a new protocol to follow, or a variation of aprior protocol. The efforts of such iterative programming are to move auser toward improvements in health management in a rapid fashion. Afurther benefit is to make more efficient the operation of the computingenvironment by saving computing cycles and battery power.

As another example of an output, a lifestyle modification may bedisplayed (step 42). The lifestyle modification displayed is commonlythe result of the step 19 noted above, i.e., to evaluate to determinelifestyle modification. For example, a lifestyle modification may be anindication such as “You did well on your attempt to control your glucoseduring breakfast, but maybe you should try eating half as much on yourWednesday meal, or walking a half hour before you eat.”

As yet another example of an output for display, a cookbook, such as inelectronic form, may be provided which is personalized to the user withrecipes determined to be likely favored by the same (step 44). Therecipes can be personalized to the user, on the basis of data aboutprior foods eaten, e.g., using retrospective user analysis, and may befurther tailored to the user by use of a questionnaire or othercontemporaneous data input by the user about preferred meal choices.Recipes can be selected for inclusion by their glycemic index orglycemic impact. For example, if systems and methods determine theuser's Wednesday meal to have a higher-than-usual glycemic index, arecipe may be proposed for the user for that Wednesday meal to lower thesame.

The cookbook may also indicate exemplary glycemic indices of variousmeals, thus allowing the user to compare the effects of one meal choicewith the effects of another. Besides showing the effects of differentmeals, the effects of a single meal may be displayed as the same wouldbe experienced by a nondiabetic person, and in some implementations ascompared to how the same would be experienced by persons at differentstages of diabetes. The cookbook could also adaptively show what impacta meal would have on the user's glucose. For instance a glycemic indexof a meal will have different effect between different people. The appcould tailor the glycemic impact to the user, or may alternatively showwhat the profile would look like if the user were exercising routinelyor not.

The cookbook recipes may be advantageously employed as part of aperturbation to a biological system, e.g., as part of a program, as thesystem may incorporate and use the known nutritional characteristics ofthe recipes as part of the entered food data, and thus the program maybe enabled to quantify the program perturbation and better evaluateresults vis-a-vis measured data.

As another example of an output, a reward may be displayed (step 46).For example, if the user has shown significant levels of control, areward may be displayed indicating that the user may consume an item oftheir choice. Other variations of rewards will also be understood giventhis teaching. For example, users may accumulate points or othercounters for exercising or exhibiting significant glucose control, andthe same may be the basis of achievements, prizes, posts to socialnetworking systems, or the like.

In many cases the displayed output will include summary metrics (step48). As noted these need not be quantitative, but rather can also bequalitative, and can provide an observance on the evaluation, and insome cases commentary on the user's result of following the selectedprogram. For example, the display of summary metrics may includedisplaying text (step 52), e.g., “Eating breakfasts according to thecookbook recipes was effective for you.”, “Eating those lunches withyour selected limits on glycemic index foods really helped yourcontrol.”, or “Wednesday's breakfast may not be your best meal.” Whilein some cases pertinent actual glucose values can be displayed, the samecan also be suppressed in many implementations for simplicity.

The displayed output may include consideration and display of glycemicindex or other data about food, e.g., caloric content. The displayedoutput may also include data in which such are personalized to a user.For example, if it is known that a certain food or exercise has aparticularly good or bad effect on a user's control, as determined byhistorical or other retrospective data, then an indication of such aparticular sensitivity may be included in the displayed output.

The displayed output may further include comparisons to other users,e.g., using data sourced from the cloud or other network source, so thata user may be enabled to compare their control with those of others,e.g., either generally or within a specified peer group, e.g., Facebook®friends. The type of display (of compared data) may be as notedelsewhere herein, e.g., via avatars, colors, text, trace data, or thelike.

Other types of outputs will also be understood, including those that arenot necessarily the subject of a display on the UI 26 of the monitoringdevice 21 (see FIG. 2). For example, data may be sourced or output tothe cloud or other network source (step 66). This data may then beanalyzed by an HCP and/or may be used in the calculation or use ofcompared data for other users, e.g., within the subject user's peergroup. As part of a calculation, such data may be employed to transformone set of data into a different set, allowing deductions to becalculated which were previously unavailable without such data and suchtransformations.

Providing the output to the cloud may be performed for a number ofpurposes. First, the same may be employed to transmit via an appropriatetransmissions protocol feedback to caregivers, family members, lovedones, peer groups, or the patient themselves. For example, where programgoals are monitored on a network or other cloud-based source, the samecan be employed to determine if the user has met a goal, e.g., a goal ofa program set by the perturbation 11 (see FIG. 1), or a goal set bycaregivers, physicians, friends, or family. Besides providinginformation, such cloud-based data enables social and competitiveactivities, e.g., within peer groups or on social networking websites.Providing outputs to the cloud, with this type of “social” character,can also be employed to improve compliance. For example, if a subjectuser has a glucose level in a danger zone, or is suffering from a severeexcursion, the “share” functionality may be employed by either thesubject user or a caregiver to contact the subject user and helpmotivate them to make better or alternate behavior choices. Such may beparticularly pertinent for elderly users. The social character allowsthe creation of an online community where users can act as “buddies” andhelp each other with motivation, such as exist in other behaviormodification programs. Such allows significant savings in computationalprocessing over the case where such communications have to betransmitted via email or other techniques.

Where outputs are sent to a cloud connected HCP, the same can determineif the user is responding to therapy, e.g., a meal program, exerciseprogram, or medicament programs including drugs, e.g., for diabetes,metformin (or other drug indicated for type II diabetes). The HCP candetermine if the user is a responder to medication based on a series ofblood tests and knowledge, e.g., via metadata, of other responders,e.g., other users on the same medications. Additional details of suchmedication aspects are described below in connection with FIGS. 41 and42.

In the use of cloud or other network sources, a CGM profile and culturalbackground can be shared, and can be used as guidance for improvement,both by the subject user and by other connected users. For example,subject users can research how users similar to them, e.g., with similarethnic backgrounds, body types, ages, etc., were able to improve theirhealth. In particular, providing outputs to the cloud also allows theconstruction of profile information which allows personalization oftherapies as noted above. For example, if particular exercises or mealchoices have been found appropriate and well-liked by demographicallysimilar users, such may be provided as potential options for a subjectuser. Such analysis is particular data-intensive and may requireanalysis and computation of a large number of users, using transmitteddata from around the world.

Users also may be provided with generic comparisons where such can becalculated, e.g., “You are in the top 10% of your peers.”, which canserve as motivational aids to the subject user. The overall availablepopulation data may be employed to see where the individual is situated,e.g., to determine and display if the user is typical or atypical. Thiscomparison may be made in different situations, e.g., the user may onlybe atypical during holiday seasons. Calculated or computed correlationsmay be made to historical data, and comparisons may be further narrowedby considering a user's best days, worst days, or the like.

Outputs can also be transmitted via an appropriate communicationsprotocol to cloud-connected health insurance carriers or administratorsof wellness programs. The outputs can indicate the effects of, e.g.,medication, diet, exercise, or other aspects of a user's lifestyle.Outputs can further be transmitted to cloud-connected manufacturers orpharmaceutical companies, ACOS, EMR storage facilities, or the like. Insome cases, insurance companies may be enabled to lower premiums orincrease benefits or provide other benefits to users if the users show acertain level of glycemic performance, e.g., if the users meet apredetermined threshold criterion for glycemic performance as measuredand determined by systems and methods according to present principles.

Returning to FIG. 3 and in particular to non-cloud-based outputs,another type of output that may be of use to users is the display of atrace graph with an envelope (step 61) (or just one or more guidetraces). In particular, this type of output may provide the measuredanalyte response to a meal along with an ideal response. For example,referring to FIG. 4, a trace graph 40 is shown in which an analyteresponse 68 is illustrated along with an ideal or preferred response 72for a meal. In the same way, the graph 50 of FIG. 5 illustrates ananalyte trace 78 is generally within an envelope formed by the ideal ormodel traces 74 and 76. In these graphs, the user's resulting analytevalue is qualitatively indicated in an easy-to-understand fashion. Itwill be understood that, for uses in programmatic learning, such maygenerally represent averages, and that the averages may be determinedusing data accumulated during the time period in which the program isfollowed. These graphs may also be replaced with various surrogates,e.g., colors or avatars, indicating whether the user is “in control”,within an envelope of control, or the like. For example, the color greenmay indicate that a user is in control (e.g., within an envelope), whilethe color red may indicate the user is out-of-control. Similarly, asmiling avatar may indicate control, while a frowning one may indicate alack of control.

FIG. 6 illustrates a user interface 26′ in which a combination of typesof outputs are provided. With the user interface 26′, a single screenmay be employed to review numerous data from a given day or part of aday. The data displayed are generally with respect to a program orrecognized pattern. For example, the user interface 26′ displays a tracegraph 60 with a morning analyte response shown with a solid line(aspects of this graph 60 are similar to those of the graph 40 of FIG.4, and such details are not repeated here). The analyte response maythen be compared to a desired analyte response according to the program,pictured with a dotted line.

The trace graph output 60 may then be combined with other outputs,including a textual output 82, which is a recommendation or suggestionin this example to try lower glycemic index foods. The user interface26′ may also provide convenient links 84 and 86 to sources for suchfoods, and by following the links, appropriate such foods may bedisplayed in the UI 26′for purchase by the user.

The user interface 26′ also shows a mid-day analyte trace graph 70, inwhich an analyte level 88 is plotted with respect to time, with one ormore threshold levels shown, e.g., a threshold 94 for hypoglycemia and athreshold 92 for hyperglycemia. A textual indication 96 is displayed,indicating conveniently to the user a present summary status. Potentiallifestyle modifications 98 are shown, in the form of tips, such as to“get up at lunch” and/or perform “exercises”. In this case the potentiallifestyle modifications may be personalized to the user, as well as totime of day. For example, exercises may be suggested (not shown) whichare appropriate for the workplace, if the output is being displayedduring the workday. Users may also enter preferred exercises, and suchmay then be preferentially displayed. For example, a user may enter apreference for walking for 45 minutes rather than swimming four laps.

Other combination views will also be understood. For example, referringto FIG. 7, a user interface 81 for a typical smart phone 80 is displayedwhere manipulation of the user interface may be via a touchscreen aswell as by one or more buttons 104. A clock 102 is shown as part of theuser interface, as well as an initial indication of an analyte or otherdata value. For example, in FIG. 7, an analyte value is shown on athermometer-type scale 108, the parameter value being shown by a line112. Dotted lines indicate threshold values for alerts. In someimplementations, a more precise or quantitative value 114 may also bedisplayed. Such constitutes low-resolution data, and by a swiping motion116, additional data may be retrieved, determined, and/or displayed,such constituting higher resolution data. In FIG. 8, another parameteris shown, e.g., “fat burn” 118, and the same indicated by a similarthermometer type scale 122 with the current value indicated by a line124, e.g., corresponding to metabolic rate. A numerical value is alsoshown by text box 126, which can be used to indicate various metricssuch as the estimated fat burn rate in g/hr, lbs/hr, the total fat burnsince initiation of exercise or from the start of day, percent ofmaximal fat burn rate, lactate level, carbohydrate burn rate, or thelike. Dotted lines divide the thermometer-type scale into four zones,which may be similar to those described below in connection with FIG. 9.Such metrics are generally only calculable using anappropriately-configured computing environment with suitable input data,generally from sensor data but also user-entered data.

Following a swiping gesture, additional data may be shown, e.g., trendand history graphs, as illustrated by the exemplary FIG. 9. In thisfigure, the thermometer type scale of FIG. 8 is expanded into variouszones 110, e.g., a low fat burning zone 136 (low physical activity), amaximum fat burn zone 134 (optimal fat burn, lipid metabolism), amaximum carbohydrate burning zone 132 (optimal cardiovascular intensity,aerobic carbohydrate metabolism), and a burnout zone 128 (anaerobicmetabolism due to lactate buildup). Textual descriptions of the zonesmaybe displayed by elements 120. A trace graph 90 is seen, which in thiscase traverses three of the zones. Finally, an instantaneous value isillustrated by the thermometer-type scale 130, in which zones orgradients may be indicated, and in particular a current or instantaneousvalue is indicated by line 138.

The above-noted displays are beneficial for, in case of diabetes, thetype II population, for a number of reasons. For example, particularlywith respect to FIG. 7 and FIG. 8, there are limited or no numbers. Abar moves up and down in the column which has different zones toindicate metabolic levels. The bar may have a width to indicate range ifapplicable. An arrow can be displayed (not shown) to indicate a rate ordirection of change. Alerting or alarming may be minimized for suchpatients, and may only be present for particular purposes, e.g., toindicate if an athlete has reached a lactate threshold. Predictivealarms may also be implemented for these purposes.

Referring to FIG. 10, metrics may also be displayed as combined on theface of a watch or other similar display. In FIG. 10, a watch 140 isillustrated with a band 142 and interface 144. Exemplary options areshown, and it will be understood that other options may be displayed onsuch a combination interface.

In the figure, metrics are shown including a percentage of maximum fatburn rate 146, a numerical indication of the fat burn rate 148, anumerical indicator of a lactate level 152, and a total amount of fatburned during the workout or session 154. As noted, numerous otheroptions will be understood.

FIG. 11 illustrates another user interface 150 which may be employed indisplaying continuous glucose information to a user. Certain aspects aresimilar to FIGS. 7 and 8 and the discussion of such aspects is notrepeated here. In FIG. 11, the analyte is represented by a glucoseconcentration 106, which is again indicated by a thermometer-type gauge155. Thresholds 156 separate the gauge into three ranges or zones, andit is seen that the current level 158 is within the center range orzone, e.g., a euglycemic range. A directional indicator 162 is providedto give the user an indication of a rate of change, e.g., if the glucoselevel is rising, falling, or staying the same. In this case, an arrowpointing up indicates that the glucose value is rising.

Such a displayed output provides numerous beneficial elements andaspects. For example, the complexity of actual glucose values iseliminated. In FIG. 11, no glucose value is given. Rather, the bar 158moves up and down in a column which has different zones to indicateglucose level. For example, the bottom zone may be red and may indicatea hypoglycemic zone. The middle zone may be colored green and mayindicate a target glucose zone. The top portion may be colored yellowand may indicate a hyperglycemic zone. The location of the bar indicatesthe current glucose range of the user.

In some cases, the bar may be provided with a significant width toindicate the range which the user's glucose is in, rather than an exactvalue. That is, in this simplified version, an exact value is no longeremphasized. A wide bar or gradient of color indicates the range ofglucose for the user.

By swiping the user interface (on a touchscreen), the trend graph may bedisplayed, which is indicated in FIG. 12. A center section 160 providesranges of glycemic zones, e.g., hyperglycemic range 164, euglycemicrange 166, and hypoglycemic range 168. To remind the user, textualindicators 172 may also be employed. A similar thermometer type gauge155, along with rate of change indicator 162, may also be employed inthis implementation. Superposed on the center section 160 may be a tracegraph 163 illustrating recent values of the analyte, e.g., glucose.

If the device is oriented to type II or nondiabetic users, limited or noalarms may be configured. Alternatively, predictive alarms may be usedand may be indicated using a blinking red or yellow arrow when thepatient is predicted to cross into a hypoglycemic range or hyperglycemicrange, respectively. An alternative way of predicting direction or rateof change may be to include an arrow 174 on the thermometer type gauge155, which indicates direction and rate of change (see FIG. 13).

As noted above, a color indicator can be conveniently employed toindicate if the user is above or below a threshold. For example, asindicated in FIG. 14, an arrow 176 may point at a location on a colorstrip 165 indicating in what range a glucose value is located. While thecolor indicator of FIG. 14 is illustrated in a horizontal orientation,the same may also be disposed vertically.

As noted above, in some cases a user population will not require asdetailed a data presentation as in prior systems. For example, andreturning to FIG. 3, colors, avatars, or other range indicators may besufficient.

A number of elements, e.g., on a mobile device, may be provided incertain colors (step 58) to indicate an output. For example, the iconcolor of the monitoring application may indicate a current status, e.g.,if the user is meeting the goals of the program or not. A backgroundcolor of the smart phone itself, e.g., a wallpaper or desktop, may beprovided with a color for the same purpose. Such color indicators canreinforce good behaviors, e.g., a user may wish to continue to see thegreen color indicator, indicating they are meeting the goals of theprogram, and such may then influence user actions, e.g., meals consumedand/or exercise performed. The system can also use a color to indicate asummary of a post-meal glucose level. For example, if following a meal auser continues to be in good glucose control, the system may indicatesuch by a suitable color scheme. In the same way, a color indicator canbe employed to show the impact of exercise on the analyte level. Forexample, a color indicator may show a yellow color, but may indicatethat the same is transitioning to green as a result of user exercise,modulating or otherwise controlling a high blood sugar level.

In another implementation, referring to FIG. 15, a tachometer or gaugetype indicator 180 may be employed (see also step 64 in FIG. 3), againdivided into different glycemic zones (in the case of glucose), with theneedle 178 indicating a current zone. In this case both colors andranges may be employed in a displayed output. FIG. 16 indicates analternative such tachometer diagram 190, where a needle 182 indicatesvarious fat burning zones as may be measured by a metabolic monitor. Acoarser implementation may indicate just two ranges, e.g., whether asubject user is consuming more calories than are being expended, or viceversa. Other implementations will also be understood. Each of theseimplementations provides certain technological advantages andimprovements as the computing environments implementing the same operatein a more efficient manner by providing users with desired informationwithout requiring the user to traverse many screens and enter manybutton presses or other user interactions to achieve or view similarresults.

However displayed, where ranges are employed, the same may beconfigurable by the user or by an HCP. Depending on the analyte responsewith respect to the range, as noted, rewards, congratulations, or otherfeedback may be provided to a user.

In some cases, and again referring to FIG. 3, ranges may be configurablebased on how well a user has learned to control their glucose. As usersemploy the systems and methods according to present principles describedherein, the ability to control analyte and other glucose levelsgenerally increases. For a new user, ranges may be made wider (step 184)so that a measure of success for the user is more easily attained. Oncethe user becomes more skilled at controlling their analyte levels,ranges may be narrowed to a “medium” level (step 186). Finally, tochallenge advanced users, with the goal of even better increasing theirhealthy lifestyle, acceptable ranges may be made narrow for such users(step 188). Such again causes the systems and methods to operate moreefficiently, increasing technological efficiency of the computingsystem.

The terms “wide”, “medium”, and “narrow”, are used here to refer to alevel of analyte control. In some implementations the terms may thusrefer to the width of a band or envelope of acceptable analyte readingsmeasured while following a program (see graphs 185, 187, and 189,adjacent steps 184, 186, and 188, respectively), or subsequent to anevent. Other definitions may also be employed, including those that takeaccount of, i.e., are partially based on, threshold alerting andalarming values, e.g., for hypoglycemia and hyperglycemia.

Where methods include steps of modifying, e.g., tightening or looseningranges, the modification of the range can occur in the step ofdetermining a new perturbation, e.g., by selecting a program with themodified range. The modification may also occur at other points in themethod as well.

Given the above teaching, various other types of displayed outputs willalso be understood. For example, heat maps may be employed to plotanalyte values. In another variation, where a user is following aprogram and a deviation from an expected trace graph is seen, e.g., adata arrangement outside an envelope as portrayed in FIG. 5, then theuser may be provided with an image (graph or otherwise) of thedeviation, e.g., a sudden rise or drop. In this way, the user mayefficiently and rapidly be provided useful and actionable informationwithout having to provide significant amounts of user interaction.

For example, and referring to the flowchart 207 of FIG. 19, analgorithm, or a portion of the application 27 (see FIG. 2), may receive(step 208) and monitor incoming sensor packets and review the same forminima or maxima in the data. To do so, the data can be compared tocriteria (step 212), including potentially predetermined thresholds orthresholds based on retrospective data analysis, e.g., the sorts ofexcursions the user has seen in the past. If data in the incoming sensorpacket is determined to match or meet the criteria, e.g., to exceed athreshold, then an alert or alarm may be caused and the same displayedon, e.g., the UI 26 of the monitoring device 21 (step 214). The alert oralarm displayed may depend on several factors, including the criteriamet, the size of the excursion, and other factors.

An example is shown in FIG. 20. An algorithm, which may be aretrospective algorithm, may look for minima and maxima any time a newsensor packet arrives. In FIG. 20, two events are shown. A first event216 starts at 227 mg/dL and ends at 213 mg/dL. A second event starts at294 mg/dL and ends at 46 mg/dL. The retrospective algorithm may checkwhether the current event excursion falls outside of threshold. In thisexample, thresholds may be defined as a certain milligrams per deciliterdifference or a percentage difference between the start and the end ofan event. Another threshold definition may be if an excursion is atleast one standard deviation outside of a typical excursion (or othermeasure related to standard deviation). In any case, thresholds may bedefined so as to ensure that only meaningful events are displayed to theuser, and that nuisance alerts are minimized. Such systems and methodsclearly provide technological advantages not capable of being providedby the prior art.

Meaningful events, e.g., those that meet the criteria, trigger an alert,e.g., a push notification, an icon on a trend graph, a badge numberincrease, or the like. As noted, depending on the type of event, e.g., ahigh glucose value traversing to hypoglycemia, or the like, the alertdisplayed may differ. For example, if the event is an excursion from ahigh glucose state to hypoglycemia, e.g., 294 mg/dL to 46 mg/dL, themessage might be “We noticed a wide glucose excursion, would you like toenter meal and/or insulin information for this event?”. To providefurther information about the event, the trend graph segment may behighlighted in a different color while the alert is active, e.g.,meaning the user has not yet cleared the alert.

The output can also serve, in some implementations, e.g., in thediabetes control area, as a “glucose coach”, i.e., as part of alifestyle modification program. In this way, the output can providepositive feedback when the user is in good control of their glucose, andcan provide teaching feedback when the user is in lesser control. Goodcontrol in this context generally means the user's glucose concentrationvalue is as expected or within a predefined envelope. The glucose coachsystems and methods according to present principles can provide rewardsupon the demonstration of sufficient control, e.g., allowing highglycemic foods a certain number of times per week, upon the attainmentof sufficient control by the user. Upon the occurrence of insufficientglucose control, teaching feedback may be provided, so as to causedietary habit changes, e.g., to motivate the user to incur fewer postmeal spikes. For example, a textual output may be indicated such as “Inthe future when you go here, order something different like X”. Theglucose coach may also in this instance prompt for additional userinformation, e.g., asking the user for information about activities overthe last predetermined time frame, e.g., six hours. For example, did theuser eat high carbohydrates, did the user fail to exercise, did the usermiss a medication, or the like. Thus, upon receipt and evaluation ofdata indicating insufficient glucose control, the system automaticallycan prompt for data entry to explain the insufficient glucose control.Similar aspects are described below in the context of a “discovery mode”employed in certain implementations.

Other implementations will also be understood. For example, the glucosecoach can further provide user feedback on weight loss, e.g., asdetermined by weight, measured ketones, calorie burn, fat burn, or thelike.

Outputs appropriate to certain users, e.g., the type II diabetespopulation, may have a simpler form than those for the type I populationsince the former generally have different needs. Consequently, andreturning to FIG. 3, the displayed output of summary metrics may includedisplaying data in a pictorial form (step 54). In particular, images offood consumed by the subject user may be portrayed in various ways aspart of the displayed output. The images may be either those captured bythe subject user, or may be generic images corresponding to food enteredby the user. For example, the user may indicate that a lunch included achicken salad sandwich. The images shown in the displayed outputaccording to step 54 may include an actual image of the chicken saladsandwich captured by the user, or a generic image of a chicken saladsandwich retrieved by the application 27 from a library, either local oronline. Thus, to address the technological difficulty of displaying mealdata, systems and methods according to present principles may use imagescaptured by the subject user or generic images to address this technicaldifficulty.

In one implementation of a displayed output, as illustrated by the graph217 in FIG. 21, images of meals may be portrayed against a time axis.For example, meals 218 a-218 e are plotted against a time axis,generally representing breakfast, lunch, and dinner of a first day andbreakfast and lunch on a second day. The meals are shown at differentheights on a vertical axis. The height of each meal can be based on aglycemic index of the meal, a peak glucose response observed followingthe meal, or on other bases which may be useful to a subject user. Forexample, instead of placing the meal image at a vertical axis distancecorresponding to glycemic index or glucose response, the size of theimage may be used to convey this information, e.g., with meals ofgreater glycemic index or causing a larger excursion in glucose valuebeing portrayed with a larger image. Thus, in this way, meal data istransformed into a particularly cogent visual rendering.

In a related implementation, not shown, the displayed output may be atime-lapse movie where the food is shown as an image with acorresponding effect also illustrated on the UI 26, e.g., with a size,color, or brightness, mapped to its glycemic response. In thisimplementation, the glycemic response may also be displayed as a tracegraph, with a “current location in time” head shown moving along thetrace graph in time along with the pictured image of the food consumed,both with respect to the passage of time.

In another implementation of a displayed output, as illustrated by thediagram 220 of FIG. 22, meal images or other indicators may be sortedinto a food pyramid or other organizational scheme, e.g., “WeightWatcher®” bins. In diagram 220, users may be informed that items in thebottom zone 226 are most preferred for glucose control, items in themiddle zone 224 are lesser preferred, and that items in the top zone 222should only be eaten sparingly. The displayed output may then sort thefoods eaten by the user, entered into the application as noted above,into the various zones. An indication may be given as to whether theresult is a success or failure. A successful result would be if theuser's meals 228 a-228 g occurred in the proper proportions according tothe sizes of zones 222, 224, and 226. An unsuccessful result would be ifthe user's meals were, e.g., “top-heavy”, in which a majority, or toomany, of the user's meals occurred in the top zone 222. Thisimplementation can give the user positive feedback, even if some of theuser's meal choices were not always preferred.

In variations of the diagram 220, the images of meals 228 a-228 g may beaccompanied by an indication of the time the meal was consumed.

In another implementation of a displayed output including pictures orimages, as illustrated by the diagram 230 of FIG. 23, a number of meals232 a-232 f are illustrated in various zones 234 a-234 c correspondingto meal categories of high glycemic index, medium glycemic index, andlow glycemic index, respectively. Similar categories may apply to high,medium, and low glucose responses following meals. The same may also besorted into caloric zones.

While a limited number of meals are displayed here, and in, e.g., FIGS.22 and 21, it will be understood that such diagrams may include allmeals pertaining to a particular program (or other perturbation), asubset of meals chosen as a representative sample, or anothersubcombination of known meals. Common meals may be combined and stackedin the display to save screen real estate. The determination of whichmeals are “common” may include analysis of hash tag information or otherdata entered by the user.

Combinations of the above pictorial types of displayed outputs will alsobe understood. For example, a number of food pyramids may be displayedalong a timeline, each food pyramid corresponding to a day, a meal, orthe like. In another variation, besides glucose, other analytes orphysiological data may be portrayed, e.g., metabolic rate indicators,weight loss indicators, and the like. Other graphical techniques mayalso be employed for displaying outputs, e.g., heat maps, pie charts,histograms, and so on. The determination of how much data to include ina particular displayed output may vary, but is generally based on thedata obtained during the current program, i.e., perturbation. In certainimplementations, additional historical or retrospective data, from othersources, may be employed in a displayed output.

Other types of outputs will also be understood, including outputstailored for type II users. In particular, certain technical challengeshave been encountered in past analyte monitoring efforts related tounderstandability and context of the displayed data. Prior displays tohave caused users to have to scroll or touch their way across numerousinterface screens to obtain desired data and such present additionalcomputing efforts required. Thus, in certain implementations accordingto present principles, calculations may be performed to display data ona user interface that is tailored to type II users, and may in somecases depend in part on user configurable settings.

In more detail, quantities which may be familiar to a type I user, suchas a glucose concentration in mg/dL, may be difficult for type II usersto understand as they may lack training in the same. In many cases, suchusers are unaware of the difference between, e.g., 55 mg/dL and 325mg/dL. Accordingly, a user interface may be more “relative” for a typeII patient, and may also be more explicit about status updates andactions to take. For example, instead of displaying 55 mg/dL, the userinterface may state “you are low”. Instead of displaying 325 mg/dL, theuser interface may state “you are high”. Instead of displaying 120mg/dL, the user interface may state “you are in target”. Differentcolors may be employed, using color schemes described above, e.g.,red/yellow/green, where a user prompt in a red area indicates thataction is required, a user prompt in a yellow area indicates a warning,and a user prompt in a green area indicates the user is in target. Insome cases the intensity of the color may change, with the intensity ofthe area in which the user is situated being made more intense orbright. That is, the bar the user is in would be brighter than the otherbars.

An exemplary such user interface is shown by the user interface 250 ofFIG. 24. In this figure, points 256 on a trace graph are shown, alongwith a glucose value of 66 (element 254). These elements are relativelydeemphasized, however, in favor of the colored bars indicating variousranges. Shown are a target range (euglycemia) 242 which is coloredgreen, yellow warning ranges (impending hypoglycemia 246 and impendinghyperglycemia 244, typically informing the user to watch and wait), andred danger zones (hypoglycemia 252 and hyperglycemia 248, typicallyinforming the user to take action right away). In some implementationsthe range corresponding to the current glucose value may be madebrighter or enlarged. In FIG. 24 the user is in hypoglycemia, and atextual indicator is provided to notify the user of potential actions totake. The same user interface with the user in euglycemia is illustratedin FIG. 25.

FIG. 26 illustrates alternative user interfaces useful for type IIpatients, and in particular varieties of home screens for a continuousmonitoring application. The screen 258 of FIG. 26A illustrates an A1cestimator. As before, a green range indicates the user is in target andshades of red may be used for out of target occurrences. A sharefunctionality can share if medication was taken or not. The user canalso enter a time delay, such that so long as medication is taken withinthe time delay, an alert will not be sent to a follower. If medicationis not taken within the time delay, the alert is sent.

The current glucose zone may be highlighted as compared to other zones,e.g., may appear brighter. As seen in FIG. 26C, the application maycommunicate the current zone with a textual notification, e.g., “you arecurrently high” or without such (FIG. 26B). Such may be especiallypertinent when lows (hypoglycemia) for type II users, as such aregenerally rarer than hyperglycemic situations.

Activation of the advice button leads to a screen as illustrated in FIG.26D. In this screen, doctor and/or IFU advice may be provided. Theadvice may be specified to the glucose state, time of day, GPS location,and so on.

FIG. 26E illustrates a reminder on an initial wake screen.

FIG. 26F illustrates a screen following activation of a “takemedication” button. Such functionality typically provides benefits forbehavioral modification. For example, a user or caretaker may set thetime medication is to be taken, along with the name of the drug and adescription of the drug. If the notification (such as may be seen inFIG. 26E) is ignored, a notification may be sent to a follower. From thenotification shown in FIG. 26E, a user can acknowledge any and all drugstaken at the current, past, or future time. This action also enters anevent on the trend graph. An additional reminder is seen by a badge onthe “take medication” button, indicating that medications are scheduledto be taken.

FIG. 27 illustrates a screen which may be employed when the user is outof or between sensor sessions. With an appropriate button, the user canadd meter readings from an SMBG. In this way, additional functionalitymay be added to monitoring applications, to monitor glucose readingseven when a continuous glucose monitor sensor is not available.

Variations will be understood of these above described user interfaces.For example, turning the smart phone may lead to a landscape mode graph,which may be pinchable/zoomable.

FIGS. 28-36 illustrate other aspects of user interfaces which may beparticularly appropriate for type II users.

FIGS. 28A-28H illustrate various types of indicators which may bepresent on user interfaces, and which may also appear in variouscombinations on a given user interface. FIG. 28A illustrates a currentblood glucose level. FIG. 28B illustrates a trend arrow, which in thisexample is horizontal, indicating the user is maintaining their level,but which may be pointed upwards or downwards to indicate a trend. FIG.28C illustrates a trace graph, which can have discrete data points or acontinuous curve. The background color may be used to indicate whetherthe user is in range or out of range. FIG. 28D illustrates a statusmessage, in the form of text, which can be particularly useful for typeII users as the same are often not used to determining the meaning ofglucose concentration numeric values. It is noted that the particulartext to show may be varied from patient to patient, and may also varydepending on the historical impact of glucose parameters vis-a-viz thatindividual patient. In other words, a number which may lead to onestatus message for a first patient may lead to a different statusmessage for a second patient. For example, for one patient a glucosevalue and rate of change may be indicated as “you're doing okay”,whereas for another patient, the glucose value and rate of change areassociated with a more problematic diabetic state and thus the statusmessage may be a warning to “take action before you go low”.

FIG. 28E illustrates daily progress, and in the implementation shownillustrates how much time in the current day the user has spent withinthe target range. FIG. 28F indicates various events, which may have beenlogged or entered by the user or which may have been detectedautomatically by the system. Where the system detects eventsautomatically, such provides technical improvements to the system asevents are generally logged more rapidly and accurately. This solves aproblem of the prior art, the problem related to a late entry of data,late occurrence of necessary calculations, late display of action items,and so on. Such also allows the ability to build a knowledge base over aperiod of time, e.g., two weeks, in order to build a deeperunderstanding of cause-and-effect for the given patient.

FIG. 28G illustrates an action indicator, with a current status “bubble”or “circle” depicted in red and offset from a neutral target circle.That the red circle is offset in a downward direction from the neutralcircle indicates that the user is below the target range. The color ofthe circle, i.e., red, indicates that action needs to be taken rightaway. A textual indicator is also provided to reinforce this message. Ifthe remedial action cures the situation, then the smaller circle colorwill change to green and the smaller circle will move to within thelarger neutral target circle. FIG. 28H illustrates exemplary suggestedactivities useful for type II users, who again may be unfamiliar withspecific remedial actions to take to address certain glucose situations.

These indicators, including in one or more combinations, have been shownto significantly induce behavioral modification in type II users.

One combination of the above components is illustrated in FIGS. 29A-29C.In FIGS. 29A-29C, the user interface shows the current blood glucosevalue, a color to indicate urgency and a graph, e.g., covering athree-hour historical past period. In addition, it will be noted that inFIGS. 29A-29C, only one colored band is shown at a time, this bandcorresponding to the current state of the user.

FIGS. 32A-32C are similar, with less emphasis on the colored bands. Inaddition, the user interface of FIGS. 32A-32C allows convenient userentry of events corresponding to meals, activities, and other events,e.g., ingestion of medication. In this way, the user may be enabled tocheck the effect of such events on their glucose levels. Having theblood glucose number attached to the graph makes it evident to the userthat the number displayed is the current value for the user. Invariations, the graph line may also indicate a future trend.

FIGS. 30A-30C provide a variation on the implementations above, in whicha trace graph is shown with a terminus at a current time point. Color isagain used, and a textual indication may indicate the status of thepatient as being in, below, or above goal. A percentage of the day inthe goal is also indicated.

FIGS. 31A-31C provide a further variation on an implementation of theuser interface. As in FIG. 28G, a “bubble” or “circle” indicator isemployed to indicate to the user whether they are below, at, or above adesired target, the desired target indicated by a larger grey circle.The status message may also be provided in this implementation toprovide an indication to the user of steps to take to address the highor the low.

Referring next to FIGS. 33A-33C, a user interface is shown in whichactivities may be logged in a proactive fashion. FIG. 33A illustrates aninitial screen, and FIG. 33B illustrates the effect of tapping one ofthe entries of the screen of FIG. 33A, i.e., “eat your favorite food”.In this implementation the user may press start, take an image of thefood they are about to eat, and consume the food. The effect of thisconsumption is shown in FIG. 33C. In the example shown, the effect ofthe consumption is to cause the user's glucose value to extend into awarning zone for hyperglycemia.

FIG. 33D illustrates another user interface according to thisimplementation, in which various types of events affecting a user'sblood sugar may be investigated further, generally by tapping theappropriate button. For example, under “eat your favorite food”, theuser's favorite foods may be displayed along with estimated effects ontheir blood glucose, either with respect to their current glucose valueor more generally. For example, as shown in the figure, eating friedchicken may cause the user's blood glucose to rise by 150 mg/dL, andsuch may be indicated in yellow to notify the user that eating such willcause them to enter a warning zone. The yellow color indicator mayalternatively be employed to notify the user that eating such is moregenerally a less optimum choice.

By displaying options in this manner, less keystrokes or button pressesare necessary for a user to enter data important to glucose monitoringcalculations. In this way, the overall computing equipment, e.g., smartphone, runs more efficiently and requires less entered button pressesfor the same output calculation.

FIGS. 34 and 35 illustrate event logging that is not proactive butrather occurs after the fact. Referring first to FIG. 34, the start of adownward trend in blood sugar is indicated along with the ensuingdecrease and a current glucose value, displayed in the color red,indicating a dangerous situation for which action needs to be takenimmediately. The application automatically prompts the user to enter anevent, if any, that might have precipitated the decrease. In this case,potential choices for events are displayed, the same corresponding tomeals, activity, or “other”, as is also described above. The user mayselect that they exercised, leading to the interface shown in FIG. 35,in which a “running” icon is used to log the event of activity whichcaused the decrease, at least as a correlation.

FIG. 36 illustrates an exemplary notification screen. Such notificationsmay be particularly useful for situations in which users are going low,as such situations have immediate and impactful consequences.

Other variations will also be understood and other types of outputmechanisms which can be employed to display the outputs above includeoutput mechanisms described in U.S. Ser. No. 61/978,151, filed Apr. 10,2014, and U.S. Ser. No. 14/659,263, filed Mar. 16, 2015, both owned bythe assignee of the present application and herein incorporated byreference in their entireties.

FIG. 37 illustrates a particular implementation of the flowchart ofFIG. 1. In particular, FIG. 37 relates to a guided start-up program thatcan serve as an introduction to analyte monitoring for a subject user.For a user with type II diabetes, the systems and methods describedaccording to present principles may provide an introduction tocontinuous glucose monitoring, and may enable beneficial behaviormodification for those users, including newly diagnosed users with typeII diabetes.

For example, for such patients, a kit, system, or package may beprovided with a glucose sensor and transmitter, along with optionalcomponents such as a heart rate monitor (e.g., using an on-skin sensor),ketone test strips or other such ketone sensors, and other types ofsensors as described above and further described below. One or moreapplications running in a mobile device, such as the monitoring device21 of FIG. 2, may be employed to receive data from the various sourcesand use the data to help those with diabetes achieve an optimumlifestyle balance incorporating a healthy diet and exercise. The kit,system, or package may also help newly diagnosed type I patients toestablish a nutrition and/or exercise plan to meet their body's uniqueneeds.

The application may be employed to receive and evaluate data determinedby the multiple devices, e.g., by identifying ratios, correlations, orthe like. The application also provides a user-friendly user interfaceto implement phases of education in both diet and exercise. The sensorsystems including CGM may be used to determine how a patient's bodyuniquely responds to programs including meals and exercise.

In one implementation, a phase 1 program may include a seven-daycontrolled diet in which different types of foods, e.g., high fat versushigh carbohydrate, fat or carbohydrate versus protein, or the like, areprescribed for a user's consumption. CGM may then be employed todetermine the effect and interactions of each type of food on thepatient. To simplify the implementation of the program for the user, theprogram may affect only a single meal per day, with the remainder of theuser's typical diet and exercise routine unaffected. For example, phase1 may include a program to try different types of breakfasts, and thesystems and methods at the end of the program display an outputresponsive to the program, e.g., displaying the best breakfasts and theworst breakfasts as determined by the monitored data, e.g., byevaluation of glucose values, glycemic indices, and other knownparameters and variables as determined or measured. Continuing with thisexample, phase 2 of the program could essentially be the same controlledmeal plan except portion size may vary, thus showing the user howamounts of food impact their glucose levels. The output may be a graphshowing the effect of the selected meal, e.g., and in someimplementations a comparison may be illustrated, e.g., with priorbaseline data, showing greater or lesser glucose control. In much thesame way, the CGM data may be employed to show the impact of exercise,e.g., both type and duration, on each individual. In this way, the typeII patient may be enabled to evaluate current behavior and identifyparticular problems and patterns, learning in the process how to modifytheir behavior in order to achieve desired results.

As another example, phase 1 may include a program to try different typesof breakfasts, and then phase 2 may be for the user to try differentexercise routines or regimes. Phase 3 can then go back to address meals.Subsequent phases or levels can also include the addition of otheraspects, e.g., to learn or determine the effect of alcohol intake,challenging the user to stay within zones or predetermined durations,limiting excursions, or the like. Numerous variations of these will beunderstood, and the phases or levels followed by the user may be thoseso as to best keep the user's interest and to rapidly place the user ona path to better health. In this way, a user can more easily andeffectively learn how to modify their behavior based on their own uniquephysiology and lifestyle patterns in order to improve their healthwithout the complexities and costs associated with a clinicalprofessional.

As a particular example, phase (or level) 1 may be employed to assistthe user in better learning about the effects of exercise. Consequently,the program may call for the user to perform a run three times per weekand to observe what the effect of the run is on the user's glucoselevel. The systems and methods can determine an exercise sensitivity,akin to insulin sensitivity, and can note the same to the user. Forexample, the systems and methods may note that “Walking seems to reallywork for you.”.

As noted in the evaluation step noted above, first-time users or otherusers who are being introduced to systems and methods according topresent principles may be provided with programs that are easy-to-followand with which it is relatively easy to obtain successful results. Asusers become more sophisticated, and more knowledgeable about theeffects of food and exercise on their lives, “successful” ranges andenvelopes may be reduced in size or otherwise tightened so as to make itmore difficult for the user to achieve a “successful” result.

In some implementations, the programs may be combined with known weightloss programs having predefined meal plans, such as Jenny Craig® orWeight Watchers®. In more detail, a “mode” may be entered in which thepredefined meal plans are defined or consistent with known diets, suchas those noted above or others, e.g., the Atkins diet, the South Beachdiet, and so on. Alternatively, meal plans may be customized for a useror user type, e.g., including programs specified to a “busy executive”,“elite athlete”, or the like. In these ways, a doctor may recommend aprogram for reversing diabetes, preventing diabetes, or continuing toprevent diabetes, in a similar fashion to the way doctors recommendweight loss programs. In some cases, “cheat days” may be situated in theprogram of such diets, not only as a reward for the user but also forpurposes of checking the measured analytes, to ensure that the dietand/or exercise plan is working.

Over time, systems and methods according to present principles mayperform “machine learning” to allow significant personalization ofprograms suggested and therapies prescribed, e.g., by utilizing priorinformation about the particular host. In this way, the computingenvironments implementing such systems and methods may be made moreefficient, requiring fewer computations to arrive at an appropriateoutput for display. Such machine learning can be on the basis offeedback by the user, retrospective user data analysis, program selectedby the user, and the like.

In this way, by increasing engagement and knowledge of a particulardisease, e.g., diabetes, users will become more engaged, more aware ofthe effect of food and exercise on their lives, and will thus becomehealthier individuals.

As noted above with respect to, e.g., FIG. 2, data used by computingenvironments implementing present systems and methods may be obtainedfrom a number of sources, including sensors, cloud or network data, aswell as user entry. It is noted here that the systems and methods mayoperate on whatever data they are provided. For example, assuming thesystems and methods have access to analyte level, if a user enters fooddata, the systems and methods will attempt to provide an evaluation ofthe food data and the analyte data with respect to the selected program.Similarly, if a user enters exercise data, the systems and methods willattempt to provide an evaluation of the exercise data and the analytedata with respect to the selected program. In addition, data may bereceived from numerous sources and not just those entered by the user orfor which the user gives an explicit acknowledgment. That is, in somecases, the application may request data from a connected device (e.g.,via a health data sharing application or “app”) and/or from the cloud onits own, and assuming appropriate rights have been granted to thesourcing entities, the data may be received from a network or othercloud-based source, even without the user expressly being aware of thereceipt of such data.

The systems and methods may also be adapted to receive data and/orsettings from other network sources to further assist personalization ofprograms to users from personal trainers, nutritionists, HCP's, and thelike.

An exemplary implementation of the use of such programs is illustratedby the flowchart 310 in FIG. 37. In a first step, a program is selectedby a user (step 300). In some cases, default programs may be provided tothe user on the user interface 26 (FIG. 2) as potential programs. Thedefault programs may be based on no prior knowledge of the user or maybe determined based on knowledge of the user including retrospectiveuser data analysis, user responses to a questionnaire, or the like.Following selection of a program in step 300, in some cases a subprogram may also selected by the user (step 301), the subprogramselected from one or more displayed subprograms related to the programselected in step 300. For example, in step 300, a user may selectwhether to follow an initial meal program or exercise program. In step301, if the user selected a meal program in step 300, the user mayselect whether to follow a breakfast program, a lunch program, or adinner program. Alternatively, if the user selected an exercise programin step 300, the user may select a running program, walking program,yoga program, or the like, in step 301. Users may be enabled to selectknown food programs having known steps and meal plans, e.g., such asthose provided by Jenny Craig® or Weight Watchers®.

The program selected in step 300, as well as the optional subprogramselected in step 301, may further include a sub-subprogram which is achallenge or scenario of the day. For example, in the case of glucosecontrol, a sub-subprogram may be issued to a user on the user interfacesuch as “Can you stay below 140 today?”.

A subject user may select a program and/or subprogram or the system maypropose certain programs/subprograms determined to be of potential useto the subject user. Moreover, upon completion of a program orsubprogram, the systems and methods in many implementations may proposea new (or modified) program/subprogram, and the same may be similarlychosen to be of potential use to the subject user. In performing theabove-noted determinations, machine learning or other sorts ofartificial intelligence may be employed. Considerations useful for suchmachine learning or artificial intelligence may include an effort tocreate healthy habits for, e.g., type II diabetic users, through the useof CGM. For example, the programs may be designed to create healthyhabits and may be further designed to be easy-to-follow by a typicallate onset diabetic, e.g., with considerations as to age and othertypical characteristics, e.g., typically over 40 years old and in somecases obese. Thus the consideration of the creation of healthy habitsmay include an attempt to break unhealthy habits, e.g., poor exercise,poor diet, often with high glycemic index foods, as well as an attemptto create new habits, such as easy-to-follow exercise routines and dietplans. Consequently, many of the displayed outputs described above maybe employed to further these considerations, e.g., by including the useof positive reinforcement, graphical illustrations of the effects offood and exercise decisions, progress reports, reminders, recognitionfor accomplishments, and the like.

To create and encourage healthy habits, an obvious trigger may be usedas an instigator, e.g., the app 27 may provide a challenge based on aglucose pattern, e.g., and may space challenges apart in time to allow auser to concentrate on just one challenge at a time. For example, thechallenge may be for the subject user to maintain their glucoseconcentration value between 100 and 150 after breakfast. The app maythus provide an attainable goal, and may further provide resources tothe user while they are trying to accomplish the goal. For example, asshown in FIG. 6, a link to a food source may provide an easy way for auser to obtain healthy food choices. Accountability for the user'sattempt to meet the goal may then be as noted above, by a display of anoutput including an indicator of success or failure, reinforcement,suitable follow-on programs, and the like. For example, if a user isunsuccessful, a follow-on program may provide more easily-attainablegoals. Particular challenges may be related to a glucose goal, amonitoring goal, may be focused on a particular time of day orparticular meal, e.g., breakfast, or on a particular activity, e.g.,exercise or medication adherence.

The programs may be selected to provide teaching to the user through aprocess of self-discovery, guided by information, outputs, and newprograms as determined by the systems and methods, sometimes incombination with user input. Advantageously, the programs may beparticularly customized for the user, without the complexity and costassociated with a clinical professional. Allowing programs to beinformed by user data allows significant technological advantages assuch programs run more efficiently, decreasing computation time andnumber of cycles required, saving battery power, and so on.

Returning to the discussion of FIG. 37, systems and methods according topresent principles may in some implementations provide initial guidancein following the program (step 302). For example, the systems andmethods may provide suggestions to help users, e.g., including textualindicators such as “Here's what you should aim for: X, Y, and Z.”.Initial guidance may also be provided by the provision of cookbooks asnoted above, e.g., with respect to step 44 of FIG. 3. For new usersunfamiliar with reading traces, textual explanations may be providedsuch as “Try to keep your trace in the good envelope or within the goodbounds.” For users desiring an even simpler display, the indicator maybe, e.g., “Try to keep this color indicator in the green zone.”.

The glucose level and optionally other data may then be tracked (step303). In particular, the glucose (or other analyte) and optional dataincluding meal and activity data may then be monitored via appropriatesensors, and the data stored. In the particular case of food andexercise programs, typically meal data and exercise data will bepertinent and thus the same will be monitored and stored. In some casesglucose and other data may then be analyzed (step 304). Such analysismay include signal processing, analysis to determine related data suchas time rates of change, or analysis to combine two or more disparatepieces of data to obtain a new resultant datum. The analysis may alsoinclude analysis of retrospective data; in other words, analysis ofretrospective data may be combined with analysis of contemporaneous orrecently-received data to determine the effect of the program on theuser's analyte level, and, by extension, overall health. In someimplementations, the analysis may include analyzing the data todetermine adherence, or lack thereof, to the program. For example, ifthe user begins a program but the systems and methods indicate little orno adherence to the same, the systems and methods may prompt the user toenter information such as “Do you recall the present program?” or thelike. The analysis may further include receiving and analyzingbehavioral and context information, as disclosed in U.S. Publ. No.2015/0119655 A1, incorporated by reference herein in its entirety.

A next step is the evaluation of the glucose trace, and optional otherdata, against the selected program and/or subprogram (step 306). In thisstep, the specific evaluation will be based on the perturbation, i.e.the program, as well as on available pertinent data. In general, theevaluation may include evaluating a parameter X and glucose, where Xcorresponds to the program's predefined instructions. The evaluation,e.g., which may be a correlation, may be against an expected, modeled,or predicted response, which may also be thought of as an idealresponse. For example, where the program constitutes a meal planintended to maintain a user's glucose level within a predefinedenvelope, the evaluation may include comparing a series of meal data tothe predefined envelope. To enable the application to identify a glucoseevent with the meal, in this example, the data can be analyzed againstthe time of day, to determine if an event data is reflecting a meal, andif so, what meal it is reflecting. Other data, including user entereddata, may also be employed and analyzed in this regard, for subsequentevaluation in step 306.

As a more specific example, if a program concerns learning the effect ofchanging a parameter such as the breakfast meal, the evaluation may beto examine the parameter versus the glucose value, i.e., a parametercorresponding to the breakfast meal, e.g., calories, carbohydratecontent, glycemic index, or the like, against the glucose response. In amore advanced implementation of such a program, the program may alsoexamine previously-determined baseline values of, e.g., “glucose duringand after breakfast”. Similarly, if the program concerns learning theeffect of an exercise parameter such as going on a run at lunch, theevaluation may be to examine the exercise parameter versus glucosevalues within a particular timeframe, e.g., 11 AM to 5 PM. Of course,overall glucose control can also be looked at and evaluated.

The evaluating may also include evaluating the effect of activity on theglucose level. Activity level may be measured and quantified in variousways, including using predetermined levels such as: sleeping, sedentary,light activity, medium activity, and high activity levels.

The evaluating may also include evaluating the effect of metabolism onthe glucose level. For example, data from a metabolic monitor, describedelsewhere here, may be combined with glucose data to identifycorrelations between metabolism and glucose control. Similar evaluationsmay correlate cholesterol with glucose data.

Evaluations may also be performed based on effective glycemic index orglycemic impact, or on the impact of fats and proteins. In these cases,a CGM trace may be analyzed to compute effective glycemic index (EGI).Over a period of several weeks, a distribution of the EGI may bedetermined and the same may be evaluated with respect to food habits,e.g., a correlation drawn, and the correlation may be short-term orlong-term. Such considerations may be displayed to the user to indicatethe beneficial effects of moderation, by identifying the ensuing EGIfollowing a particular meal. For example, after eating a doughnut, theuser may be presented with a suggestion such as, “Perhaps it would be abetter glycemic choice to eat half as much next time.”.

In a final step, an output is displayed responsive to the evaluation(step 308). The displayed output may be any of the types describedabove, depending on application, and exemplary displays are noted belowwith respect to the specific examples.

In one exemplary implementation, illustrated by the flowchart 320 ofFIG. 38, a user selects a program to start their engagement with thesystem, and in this example the user selects a meal program (step 312).The user then selects a subprogram, concentrating on the breakfast meal(step 314). Systems and methods according to present principles theninstruct the user to limit high glycemic foods or high carbohydratefoods (or to generally limit foods with significant glycemic load orcarbohydrate content) during breakfast, but otherwise to vary the typesof breakfasts they eat (step 316). Glucose level is then tracked, alongwith food data as entered by the user (step 318). In some cases, ifdesired, exercise data may also be entered by the user (or otherwiseobtained) and subsequently tracked. A data analysis step may beperformed if desired or needed for subsequent calculations (step 322).Besides limiting high glycemic load foods, other programs may includeinvestigating the impacts of fats and proteins.

The glucose trace is then evaluated along with the entered food data(step 324) against the selected program (again, exercise data may beevaluated as well). Results may then be subsequently displayed (step326).

For example, the evaluation and displayed output may show to the userthat eating eggs for breakfast provides better glucose control thaneating doughnuts, and in particular provides better control overall. Inother words, it is not just that one meal is shown to be better thananother for control, but that overall, as compared to a prescribedprogram, a pattern of certain meal choices may provide for betterhealth, and systems and methods according to present principlesdemonstrate such to a user, and further provide for increasing benefitsto health, as the programs iterate, and the user becomes increasinglymore educated about how to make better choices in meals and exercise.

Another example is illustrated by the flowchart 330 of FIG. 39. Methodsaccording to the flowchart 330 may take various forms, but many may beimplemented within the context of an already-selected program as notedabove. A first step is that a user selects a particular subprogram,e.g., “MY G.I.” (step 332). This particular subprogram is intended to bea training tool, and generally (or in many cases) to be implemented inthe course of another program, e.g., to educate the user about theeffects of certain food items. This is in contrast to the educationalgoals of most programs according to present principles, which seek toeducate the user with respect to overall patterns of eating, exercise,or the like.

The subprogram of step 332 uses the concept of a glycemic index as notedabove, which is a characteristic of an individual food. However, thesame may be specified to a particular individual, as the glycemic indexmay vary depending on the user's particular choice of a specified food.For example, a glycemic index of a medium-sized potato may have acertain value, which can be determined by the user using any number ofreferences. However, what constitutes a “medium” potato may vary fromuser to user. The particular glycemic index, specified to a user'sintended food item, is herein termed a “glycemic impact”. Thus, oneaspect of the subprogram of step 332 is to determine data about what auser defines as a particular food item, and in so doing, defining theglycemic impact of the particular food item. Advantageously thispersonalization of glycemic information avoids the complexitiesassociated with standardized regimes, which may be too rigid for manyusers to utilize practically.

Another aspect is that a beginner may have a certain goal with respectto a food, the food having a certain glycemic impact, while anexperienced user might have another goal. For example, the experienceduser may be aware that eating smaller portion sizes, eatingcarbohydrates in combination with proteins, performing exercise beforeeating, eating more slowly, allowing more time between insulin and ameal, may all lead to enhanced glucose control.

As the final arbiter of glucose control is the measured glucoseconcentration value, following selection of the subprogram, the glucoselevel may be tracked, along with data about foods consumed (step 334).The data may be analyzed, e.g., to determine the glycemic impact (step342). For example, a photograph of the meal may indicate a potato, andthe user may enter data indicating that the potato is a medium-sizedpotato. The glucose level is tracked, and a portion of a subsequentglucose rise may be attributed to a medium-sized potato, this definitionbeing used in subsequent calculations.

As before, the glucose value and food may be evaluated against theselected program (step 344). In this case, if the user provided a“guess” as to the glycemic impact prior to the meal, the guess value maybe compared against the actual value. The user's guess may constitute arise in glucose value, but particularly-experienced users may even guessa glucose trace waveform by dragging their finger or stylus on atouchscreen interface, where such is provided by the monitoring device21. Even if no guess was made, the entered food data and the resultingglucose level and/or glycemic impact may be evaluated, and an outputdisplayed (step 346). For example, the output may be displayed as aglycemic impact of the entered food, either as an absolute value or as acomparison to other foods consumed by the user, including like mealssuch as other dinners, or with like constituent elements, such as “mealscontaining chicken”.

Using subprograms such as those selected in step 332, users may beeducated as to the glycemic impacts of various foods. Users training onsuch programs over a time period, e.g., 28 days, may obtain significantinformation about how to eat to reverse or avoid diabetes. As notedabove, such programs may focus a week on breakfast, a week on lunch, aweek on dinner, etc.

In an advanced implementation of the above, a user may enter not onlymeal information but also post-meal bolus information. The applicationmay then ask the user what they guess their glucose level to be after 30minutes. Subsequent to the expiration of the 30 minute time frame, theapplication can show the actual glucose level and the guess, thusproviding advice on how to make the difference smaller in the future.

In yet another variation, an initial screen of the application 27 maybeconfigured to not indicate to the user their glucose level, or any sortof trend graph. Instead, every time the user opens the application, oron an occasional basis, the application may ask the user to guess theircurrent glucose value, thus training the user to estimate the glucosevalue based on how they feel. Such may serve to effectively demonstrateto the user the poor correlation between how they feel and their glucoselevel.

The flowchart 350 of FIG. 40 illustrates another implementation ofsystems and methods according to present principles. In this case, thesystems and methods react to a detected pattern, generally a deleteriousone, and propose one or more programs to address the pattern. Systemsand methods according to the principles of FIG. 40 further allow andenable a “discovery mode”, in which additional information may beobtained to better address deleterious events or trends.

In more detail, in a first step, user data is evaluated to determine apattern for which improvement may be desired (step 352). In this step,generally, retrospective user history is analyzed to detect a problempattern, e.g., a pattern of overnight lows, weekend highs, postprandialspikes, or the like. As used in this specification a pattern isgenerally a repeating data arrangement occurring over a time window suchas on a daily or weekly basis. While examples of patterns are describedabove, patterns may generally relate to sudden rises, sudden drops,repeating differences from typical or baseline patterns, or the like.

A next step is that the computer environment, e.g., application 27 ofFIG. 2, selects a program to improve the pattern (step 354). Inselecting the pattern, the computing environment may base the selectionon a number of factors, including the retrospective user data, what thepattern is of, and other factors. For example, look up tables,artificial intelligence, rules-based systems, expert systems, machinelearning, or the like, may be employed to make this and otherdeterminations. By basing the selected or suggested programs in thisway, a technological efficiency is gained because the user need nottolerate a significant amount of “trial and error” in finding thecorrect and appropriate program.

In some cases, two or more potential programs may be determined to beappropriate and displayed, and the user may have the option to selectone of the displayed programs. As above, the program is generally to beimplemented in a patterned or systematic way, e.g., asking a user to eataccording to a certain diet or perform a certain pattern of exercises.By so doing, an effect of the program can be determined vis-a-vis thedetermined pattern, and the effect can also be quantified.

Once a program is selected, initial guidance may be provided forfollowing the program (step 356). In this case, the initial guidance canalso indicate additional aspects, e.g., may indicate a potential causeof the deleterious pattern. Additional details about such may be foundabove, in the discussion of step 302.

In a variation, as the pattern is generally determined on the basis ofcomputing environment observations or determinations of a deleteriousevent or pattern, a discovery mode may be entered (step 358) in which anattempt is made to glean, infer, or otherwise receive additionalinformation helpful in determining the program of step 354, and in somecases even helping to discover a root cause of the pattern detected instep 352. The discovery mode may prompt the user to answer certainquestions over a period of time in order to obtain the notedinformation. The period of time may be a period of several hours orseveral days. The discovery mode may automatically generate and posequestions for user data entry based on glucose variations or the like,prompting for data which may relate to events causing or caused by suchvariations. The discovery mode may ask questions relating toretrospective user data, and may also ask questions relating to currentevents, e.g., current excursions or recently-experienced excursions. Inso doing, the discovery mode can perform analysis on the received data,including previously-known or historical data, and provide an output tothe user relating to, e.g., potential causes of the deleterious pattern.In many cases, the determined information may also be used as feedbackin order to inform the selected program of step 354, or to betterdetermine the root cause of the pattern (step 352), so as to provide abetter program for user self-discovery in step 354.

As one example of the discovery mode, following an unhealthy excursion,e.g., if a glucose concentration value exceeds 200 mg/dL, or a change inglucose concentration value over a predetermined period of time meets acertain criteria, e.g., exceeds a predetermined threshold, the user maybe prompted for extra information, e.g., meal details, the last timethey exercised, and so on. In this way, the discovery mode provides aprompt for data similar to a log book, but where the user only entersdata if a significant event is detected. In a variation, the thresholdsmay be adjusted so that requests for information do not exceed a certainpredetermined threshold, to avoid user interaction fatigue.

The remainder of the flowchart 350 of FIG. 40 is similar to that of theflowchart 310 of FIG. 37. In particular, glucose data (and/or optionalother data) is tracked (step 362) and analyzed (step 364). The trackedand optionally-analyzed glucose data is then evaluated against theselected program (step 366), and an output is displayed (step 368).

As a specific implementation of the flowchart 350 of FIG. 3,retrospective user data may be evaluated (step 352) and may determinethat the user has a deleterious pattern of overnight lows, e.g.,commonly experiences hypoglycemia at night. A discovery mode may beentered (step 358), and the same may determine that the user eats a verysmall dinner and works out at a gym after dinner. A program may bedetermined by a computing environment (step 354), and the program maypropose that the user eat a larger dinner and work out before dinnerinstead of after. Initial guidance may be provided (step 356), basedautomatically on entered data, and the initial guidance may suggestrecipes for the user, or may suggest, e.g., a certain level of glycemicimpact which may be beneficial for the user to attain at dinner. Overthe duration of the program, e.g., a week, the user's glucose level maybe tracked (step 362), along with their meal data and activity data. Themeal data may be analyzed (step 364) to determine to determine aglycemic impact of the meals eaten. The glucose trace, and other dataincluding, e.g., glycemic impact determined by an analysis step, maythen be evaluated against the program. For example, the individual mealsmay be evaluated against nutritional data of the meals suggested by theprogram. An output may be displayed (step 368) indicating whether theuser met the goals of the program. For example, the output may indicatethat overall the user decreased the number of nighttime hypoglycemicevents. However, a more granular output may indicate that certain dayswere better than others. For example, the user may still experience asignificant overnight low on Friday, but a subsequent discovery mode(step 358) may reveal that the user takes a long bike ride withcoworkers during Friday lunches. A subsequent evaluation of suchadditional information may lead to a modified program regimen, wherebythe user is suggested to consume a high carbohydrate bar before, during,or subsequent to the bike ride. The output of this modified program maythen be displayed in a subsequent display step. Such iterativeprocessing provides the user with progressively more information abouttheir disease and its response to therapy, and thus the user achievesgreater control over their disease and a subsequently improved healthylifestyle.

FIG. 41 illustrates another application of the flowchart 10 of FIG. 1.In particular, a flowchart 400 is shown which allows for apersonalization of therapy, in particular with regard to theadministration of medicaments. Systems and methods employing theflowchart 400 can be used to provide suggestions to users of medicamentprograms to follow, not only with regard to data discovered about theuser but also with regard to data about similar users, sourced from thecloud or other network-based system. In particular, if users havesimilar profiles, it may be assumed that similar programs andmedicaments might be appropriate.

It is further noted in this regard the practical impossibility ofphysicians to be aware of every medication and the details thereof, aswell as all of the other aspects affecting user health. Moreover, it isimpossible for physicians to be aware of details of all other users of amedicament for a particular disease, or even a subset of such users,e.g., a network-connected subgroup. Systems and methods according topresent principles, e.g., shown in one implementation in the flowchart400, employ aspects of a cloud-based ecosystem to resolve the complexityof information using the principles of numerical computation. Inparticular, users can be compared for demographic information, and thesame may be compared in a number of ways, e.g., using machine learning,artificial intelligence, neural nets, Bayesian analysis, and other knowncomputational techniques, which are required to be performed, in somecases in real time, by a computing environment, and in particular acomputing environment connected to other computing environments, andwhich cannot be performed otherwise. Besides analysis of demographics,glucose (or other analyte) trace data may be analyzed by the system, incombination with patient demographic information.

Besides the optional use of cloud-based or other network data, systemsand methods according to present principles may use the above-notedprogrammatic method to customize a therapy for a patient, includingmedicament titration, to identify a drug regimen which provides the bestresults for a user, a drug regimen including an identification of adrug, a dosage amount, and an administration pattern, including timesand frequencies. Besides providing a way to resolve the computationalcomplexities noted above, systems and methods according to presentprinciples solve the problem of users spending potentially many years onpersonally inefficient or ineffective drugs. The drug titration methodmay also include consideration of meals and exercise, which can also bepre-planned and evaluated along with drug titration to provide the bestand optimized therapy for the user.

Turning to FIG. 41, in a first step, a program is selected which mayinclude a regimen, e.g., regimen X, for a medicament. In some cases, acombination or “cocktail” of medicaments is used. In some cases, theprogram selected may simply be the first prescription provided by anHCP. In other cases, an application, e.g., the application 27, suggestsa program to follow, and the same may be based on local data such aspatient information and retrospective user data analysis, e.g.,including historical glucose data, or the same may also be based oncloud or other network data (step 384). In some cases the program canfurther be based on user input, either by the user solely or incombination with HCP input or input from application 27, the sameemployable to analyze user data and provide suggestions. Other such datamay include drug cost data, user insurance information, and the like. Inall of these data inputs, if there is a lack of response in a definedtime period, or a lack of provision of data from the user, theclinician, pharmacy, case manager, may be notified.

In this regard, as well as with respect to“computing-environment-performed” or “computing-environment-assisted”program selection under the flowcharts described here, a cloud-basedecosystem may enable HCPs and their patient users to tailor therapy andcare, including, e.g., diet, lifestyle, medications, devices, and thelike, to optimize adherence and clinical outcomes. In so doing, theapplication 27, alone or in combination with a network or cloud sourcedapplication, or an application performed entirely on a network, e.g., aweb application, may allow entry of relevant user health and lifestyleinformation. In some cases, all or a portion of such data entry may beautomatic. Based on the information, the HCP and/or user may “walk”along a decision tree. The health information may include geneticinformation, CGM data, insulin data, demographic data, laboratory data,glucose profile, and other known data as may be required or helpful.Based on the data entered, as well as patient lifestyle preferences, thesystems and methods may determine an optimal course of action withregard to the issue in question, e.g., diet, lifestyle, medications,e.g., a starting dose, devices, or the like. Optimality may bedetermined in terms of adherence, outcomes, and cost. The systems andmethods may advantageously employ genotype clustering, discriminating“similar” from “dissimilar” patients with respect to predicted responsesto medications or other varied parameters. The systems and methods canalso employ “recommender” systems, which can determine from lifestylepreferences what therapy and care is most likely to lead to adherence,and can also employ bio informatics, health informatics, andtelemedicine tools. As systems and methods thus improve clinicaloutcomes and reduce costs, the same are valuable to insurance payers aswell as to users and HCPs.

Returning to FIG. 41, following program selection, regimen X is thenfollowed (step 374) by the user. In so doing, a glucose level may betracked (step 376), and other optional data may be tracked as well. Thedata may be as noted above, e.g., received from activity sensors orother analyte sensors, received from user entry, including data aboutfood consumed, and so on. Data may also be received from the cloud orother network-based sources.

CGM data may be used, alone or in combination with other data, todetermine a starting dose of insulin as well as a starting dose of othermedications. The CGM data may also be employed to determine theaggressiveness of adjustments, and the frequency of the same. Forexample, if all glucose data is below 180, basal insulin may be startedat 20 units (e.g., versus 10 units). If some days fall below 150, 10units may be employed. If the low is between 150 and 180, 15 units maybe the starting basal insulin dose. If glucose stays above 180 over twodays, insulin may be increased by 10 units. If the glucose concentrationvalue falls below 180 but above 150, the regimen may call for waitingthree days and increasing the basal insulin by five units. If theglucose value falls below 130, the regimen may call for waiting a weekand potentially titrating by three units. Where oral medications areused, if the glucose value stays above 180, the regimen may call forstarting a combination therapy. If in one month the glucose value staysabove 150 with no lows, GLP-1 or basal insulin may be added.

As with the other flowcharts, a step may then be employed of evaluatingthe glucose level and optional other data against the medicament ofregimen X (step 378). The evaluation may be as noted above, and thisstep may further include evaluation of cloud-based data, e.g., aboutsimilar users. In this analysis users may be profiled to determinesimilarities with the subject user, as it may be assumed that, if a drugand drug regimen is successful for users similar to the subject user,the drug, drug dose, and drug regimen (or a close variation thereof) mayalso be successful for the subject user.

By use of cloud-based data about similar users, data may be collectedand analyzed about thousands, tens of thousands, or hundreds ofthousands of patients, and a profile may be selected for a subjectpatient based on significant amounts of data about the subject patientas compared to the stored data about all known patients. A clinicianwould not be able to perform this analysis manually or in any reasonableamount of time. Moreover, such a therapy optimization database may beconstantly updated with new patient data, so the same is alwaysimproving, which is also something not humanly possible by a clinicianat this scale and detail of data.

An output may then be displayed (step 382), where the output isresponsive to the evaluation. The outputs may be as noted above,indicating a degree of success or failure of the drug and regimen on,e.g., the user's glucose profile, and may also include a modification ofthe program, i.e., a new or modified perturbation. The success orfailure of a drug and regimen may include a determination as to whetherthe patient's results are the same as the expected results. If theresponse of the patient is not usual or typical or as expected, it maybe assumed that the medicine is either ineffective or is not being takenproperly. In particular, the modification may suggest a different drug,a different regimen for the same drug, e.g., increasing or decreasingunits of the drug, dose titration, frequency of titration, or other suchmodification. Suggestions may be iterative, e.g., provided daily orperiodically. Notifications may be triggered and sent to a pharmacy,clinician, case manager, and so on.

In some cases, a glycemic pattern may be detected and analyzed, in somecases including using data from the cloud or on the device, to determineone or more potential medicaments which may be of particular benefit tothe user. The output may further include a step of sending the result tothe cloud or other network, so as to allow the data to be used for thebenefit of other users and for comparison to other users.

In some cases, to detect patient compliance or lack of compliance,systems and methods may be employed which identify a signature of anoral medication, e.g., by use of a sensor/transmitter within themedication and a health monitor, e.g., a patch, to detect if the userhas taken a medication, when it was taken, and so on. In some cases,such systems may further include sensors such as accelerometers todetect exercise, or the like.

By use of the systems and methods noted above, not only can a computingenvironment more efficiently “hone in on” a preferred or optimum drugand regimen, but the patient benefits by having to take fewer drugsbecause they are taking the right drugs. Many benefits ensue, as will beunderstood, including cost savings, more rapid titration to target, andfewer side effects.

As a specific example, systems and methods according to presentprinciples may be employed to determine a preferred regimen forcontrolling glucose levels in Type II diabetes. Referring to FIG. 42, aflowchart 450 is illustrated for such a determination.

In a first step, the program may be selected for type, timing and/oramount of medicament (step 386). As above, such may employ input fromthe user, from an HCP, and/or from the cloud (step 387). The program maythen be employed over a predetermined time period (step 388). Glucoselevel may then be tracked (step 392), as well as optionally other data,in particular, insulin data, food data, exercise data,previously-measured data, laboratory data, demographics, starting dose,and the like.

The glucose level and optional other data may then be evaluated againstthe regimen (step 394), and an output displayed (step 396). The outputmay include a change to the type, timing and/or amount of medicament,when the medicament is insulin, a change to basal insulin dose, anaddition to the regimen of insulin sensitizers or secretagogues, achange of type of insulin, and the like, may be included within thescope of recommendation. The process may be iterative, e.g., daily orweekly or the like.

To assist in this method, a direct insulin sensor may be employed tomeasure the insulin sensitivity of a user. In particular, a user'ssensitivity to insulin will determine how much insulin a user needs inorder to maintain proper glucose control. Insulin sensitivity can varybetween patients and even over time with the same user.

Accordingly, in one specific implementation, the system and method mayuse a direct insulin sensor to detect the amount of insulin in a user'sbody. By combining the information of glucose and insulin concentrationinto the evaluation step 394 of the method of FIG. 42, the insulinsensitivity can be even more effectively calculated. Information aboutthe insulin sensitivity can then be used to determine if an insulinsensitizer, insulin secretagogue, or insulin, should be used to managethe diabetes. The amount of insulin or other medicament can then beincreased or decreased in such a way so as to best control the patient'sglucose levels.

In another implementation directed to physician and other userinteraction with the system, systems and methods according to presentprinciples can address the technical problem that current computingenvironments are unable to unambiguously determine or calculate a planor scheme for an HCP to follow in determining a proper medicamentregimen for a user. Instead, such HCP's rely on “what worked before”, astandard of care laid out by an endocrinologist, or other imprecisemeasures. In present systems and methods, data inputs, especially CGMdata inputs, are used to allow an automatic determination of a drugscheme to follow. Of course, above-noted systems and methods directed totitrating medication may be used to refine the determined drug scheme.

In yet another implementation according to present principles, it isnoted that different healthcare professionals help diabetic patients tothe extent that their training allows them to. These different HCP's areused to treating patients in different ways and with different types ofdata. For example, endocrinologists typically treat diabetic patientsthat have other morbidities besides diabetes. Such HCPs are particularlytrained to analyze CGM traces and draw conclusions therefrom.

However, a typical type II or type I patient will be handled by aprimary care or family care/internal medicine physician. CGM may stillbe provided to these doctors, but in a simpler form. In most cases theseHCPs are not likely to be able to parse all the nuances of datasignatures in the CGM trace; but the data may still be provided to themon a recurring basis so that they may determine longitudinally theeffect and success of the drugs they are prescribing to the patient. Inmost cases generally there will still be an overseeing endocrinologistover the medical care group, determining a preferred drug regime or setof drug regimes. The endocrinologist can provide a detailed report thatwill go out to all of the family care/internal/primary care doctors, andthat will be based in part on reimbursement and the like. But as notedthe report will be a simplified version, as such doctors are notspecialists with endocrinology and are likely also dealing with otherissues of the patient, e.g., high cholesterol, and so on. Because ofthis way in which most type I and type II patients are cared for, anautomatic determination of an initial drug regime, one that is unbiasedand personalized to the patient or user, may be of particular benefit.

In prior efforts, most HCP's would start a type II user on the samedrug—metformin. If this did not provide satisfactory results by itself,the HCP would add a second therapy, e.g., a second medication. Such maybe, e.g., a sulfonylurea, a GLP-1 drug, a DPP-4 inhibitor, or one ofseveral more.

Present systems and methods allow a data-driven approach to determiningwhat this next drug should be, or even if the initial attempt atmedicament therapy should be based on metformin, a combination ofmetformin with another drug, or should start off with a different drug.

For example, referring to the flowchart 420 of FIG. 43, a first step maybe seen as receiving CGM data (step 402). This CGM data may constitutecurrently measured CGM data, or may have been measured in the past. Inthe same way, a step may include the reception and use of historicaldata (step 406). A next step is to analyze or evaluate the CGM data todetermine if characteristic signatures are present (step 404). Inparticular, this step involves the numerical evaluation of CGM data,e.g., over the last several months, to determine if one or moreparticular curve features are present. This step may involve placingcurve features in “buckets” or “classifications” as described above.Generally, such characteristic features involve looking at glucose curvetrace rises, plateaus, falls, associations with known data about events,e.g., meal data, exercise data, sleep data, and so on, and theconstruction or calculation of numerical deductions and inferencestherefrom.

Such characteristic features may be associated then within the computingenvironment with certain defects, e.g., in insulin production and use,and the calculation and numerical inference of a defect can lead to aparticular drug being suggested, or combination of drugs. In some cases,this data may be used in combination with other data, e.g., event dataas noted, or other analyte trace data, other physiological parameterdata, and so on. In most cases the use of CGM data, via an indwellinginterstitial sensor, is particularly important for the analysis, as theuse of data from such a sensor allows the calculation and numericalinference of characteristic signatures and defects, such inferences notpossible using prior art devices, e.g., SMBG.

Once an appropriate drug or combination of drugs is determined based onthe characteristic signatures, the same is provided to the patient as aprescription for a drug regimen. The user attempts the drug regimen(step 410). Using CGM, a subsequent glucose trace is measured (step412). Other analytes may also be monitored. Using the CGM glucosetraces, and in some cases other analyte traces, systems and methodsaccording to present principles determine if the defect is stillpresent. If it is, the dose may be changed or an additional or differentdrug or combination of drugs tried, using, e.g., the titrationtechniques described above. If the defect is no longer present, it maybe assumed that the drugs calculated are appropriate. If the defect ispresent but to a lesser degree, systems and methods according to presentprinciples may calculate that a different dosage is indicated. In anycase, the new glucose trace measured in step 412 may be fed back intothe general method in step 402. Numerous variations of this techniquewill be understood.

In the development of a baseline for such determinations, and referringto the flowchart 430 of FIG. 44, a baseline is generally developed forwhat constitutes “healthy” or “normal”. Such may also be termed a “nodefect” model. For example, such data may be determined from users whoare young, healthy, and physically active. Such users may wear a CGM andprovide CGM data to a computing environment according to presentprinciples (step 414). Such users may also be requested to annotateevent data, e.g., events in their lifestyle, e.g., meals, physicalactivity, sleep, stressful events, and so on, and the same are similarlyprovided to the computing environment (step 416) so as to determine aset of characteristic healthy glucose trace signatures. In some casesthe baseline data will also include other physiological data known aboutthe patient, e.g., other analyte traces, e.g., insulin, glucagon,lactate, and so on. The measured indices from the healthy patients thusprovide a baseline for the determination of characteristic signaturedata from diabetic patients (step 418).

CGM data (and other available data) may then be received from a diabeticuser whose medicament regimen is to be determined (step 422). The CGMdata is compared with that of the healthy user data. Specificdifferences may then be identified between an individual who is healthyand one with diabetes. In some implementations, access may be used topublic domain human computational models (e.g., the Oral Minimal model)to tie the differences to underlying changes in the biology. If such amodel is used, or if another model is used, the parameters of the modelmay be informed by various analyte measurements. In some cases, suchmeasurements do not even require invasive techniques. For example, somemay be based on skin conduction, saliva, and so on.

Systems and methods according to present principles identify on acomputational and automatic basis defects or abnormalities bycomputational analysis of glucose traces, such analysis not beingcapable of being performed without computational aids.

For example, if the user's postprandial peak is calculated to occur at alater time than in the healthy individual, issues with glucagon may benumerically inferred, and the same may be calculated as being due toincretins not being released fast enough when the person is eating. Incontrast, if the postprandial peak is higher but generally follows thesame curve, then systems and methods may numerically infer that there isa problem with insulin secretion. Generally, such determinations areperformed by numerically overlaying the CGM streamed data on event dataas well as overlaying the same on other analyte data, e.g., dataassociated with C-peptides, insulin, glucagon, and so on.

In medicament determination, systems and methods according to presentprinciples calculate using known drug effects (e.g., via a lookup tableor other computational technique) what is necessary to make the user'scurve look normal, i.e., what would be necessary to make the diabeticperson's curve looked like a nondiabetic person's curve. In this way thesystems and methods can automatically and computationally calculate atreatment recommendation. The systems and methods can further provideand display a reason for the recommendation. It should be noted that thecalculated drug regimens thus provide a personalized drug regimen forthe user, and one that is identified as being optimized andpersonalized. Generally the calculated drug will be effective for theuser, with slight refinements possible related to dosing and perhapscombinations with other drugs or therapies. In this way, significanteffort is saved over prior cases where HCPs “tested out” a therapy todetermine if it had a reasonable effect on the patient, testingmedications by trial and error, and so on.

As noted, the patient may be followed to determine if the patient'sresponse was good, or if additional titration or optimization of thetherapy is needed.

Systems and methods according to present principles may be iterative.For example, a first goal of the medicament regimen may be to cause theuser's “wake up” or “awakening” glucose level to be lower than a currentlevel. In this case, sulfonylurea may be prescribed. Such a prescriptionmay generally itself lower A1C levels. Next, a user's postprandial peakmay be addressed, e.g., using a GLP-1 type drug. Systems and methodsaccording to present principles may rank defects or abnormalities interms of dangerousness to the user, and automatically calculate drugtreatments based on this priority. When a subsequent defect isaddressed, the systems and methods may ensure that prior treated defectsremain treated and are not caused to reappear given subsequent drugadditions or titrations.

Other data may also be employed in the determination of a drug regimen,or indeed any therapy regimen, besides or in addition to CGM data, otheranalyte data, and so on. For example, such data may include the contentand type of apps on the user's smart phone, which can indicate if theuser is used to exercising or keeping track of meals. Other data mayinclude what therapies have been tried in the past, and what was theuser's adherence to those therapies. Monitoring and using such data mayprovide substantial predictive power to systems and methods according topresent principles, and may indicate, e.g., what users are goodcandidates to address diabetic issues via exercise and meal controlversus via drug regimens. For example, if the user has shown a goodability to control meals and exercise, behavioral therapies may beattempted before an immediate recourse to drug regimens. Other data mayinclude, if available, insurance policy data, so as to determine whatdrugs are covered by the user's insurance.

In yet another implementation, but based on the description above thatdifferent types of HCPs generally perform best when provided with datatailored to them, an HCP login to an EMR system may cause presentationof a user interface that is tailored to a particular type of HCP (butwhich may also include a degree of user-configurability). In someimplementations, a common database is accessed, but the data istransformed into a form using a data accessor or relator to transformthe accessed data into a form best suited for the type of HCP asdetermined by their login data or metadata. For example, anendocrinologist may be provided with glucose trace data. A family carephysician may be provided with summary CGM data, e.g., “history ofovernight lows”. A nurse may be provided with questions to ask the user,e.g., “are you eating before bed?”, “can you fit some exercise into yourschedule?”, and so on.

A benefit of maintaining the common database access is that data isalways available. In contrast, transformed data may cause a loss ofdata, and an attempt to re-transform such transformed data may fail.Thus, access to untouched (by the transformation) “raw data” isdesirable, and such is accomplished using a common database scheme.

FIG. 45 illustrates a flowchart 500 in which the flowchart of FIG. 1 isparticularly applied in the area of sports optimization. In particular,it is noted that for athletes training and diet are intimately linked toperformance. Tools that provide real-time feedback of physiologicalparameters may enable individualized optimization of exercise,maximizing the benefit to the athlete. For example, an elite enduranceathlete can prevent energy depletion and poor performance by optimizingthe type and volume of caloric intake. Additionally, athletes whoadequately replenish their energy stores post-workout can avoidtriggering adverse hormonal responses from their bodies. Such needs aregenerally individualized and are not capable of being understood withoutan appropriate computing environment to measure the individual'sresponse to a sufficient number of performance impact variables.Continuous glucose monitoring in the context of the tools noted here canhelp guide and inform elite athletes on proper diet and caloric intakein response to different training programs and regimens.

In systems and methods according to present principles as applied tosports optimization, programs are provided to users such as athletes,and iterative use of the programs is intended to lead the user toward adesired goal, i.e., the determination of a fitness regimen in which theuser's goal is most closely met, e.g., with regard to muscle building orrepair, an optimized intensity level for cardiovascular health,optimized meal plans, optimized endurance, or the like. While glucosemay often be measured in such endeavors, the same may in someimplementations be replaced or combined with measurements of lacticacid, testosterone, cortisol, and so on.

As with prior flowcharts, a program is selected (step 502), as well aspotentially a subprogram (step 504). Programs and subprograms may varywidely, but they may be generally configured to be implemented in apatterned or systematic way. In many cases, they may be associated witha goal, and the goal may be to cause a physiological quantity to achievea certain level or to be situated within a certain band or envelope. Forexample, the desire may be to cause a heart rate to increase to acertain percentage of maximum, and to be maintained within a certainband of heart rates. As another example, the goal may be to cause alevel of muscle building or repair to be situated within a certain bandor envelope of values for a predetermined period of time. In some cases,a single threshold will provide suitable criteria, and it may be desiredthat a physiological quantity simply stay below or above the threshold.Exemplary physiological quantities include metabolic rate, heart rate,intensity level, and the like.

In some cases, such as with metabolic rate, analytes may be measuredfrom which the quantity may be determined or derived. In other cases,analyte monitoring may be performed for other purposes. As particularexamples of analytes which may be useful to monitor for sportsoptimization purposes, the following examples are provided: acombination of lactate and heart rate monitoring, a ratio R of VCO2/VO2,glycerol, ketones, a combination of glucose plus lactate, or acombination of glucose plus activity, where activity is shown, e.g., byGPS or accelerometer. Details and examples of the use of suchmeasurements are provided below, as are additional details and examplesof sensors to monitor the various analytes.

An optional step of providing initial guidance may be conducted (step506), and the same may indicate suggestions to achieve success in theprogram, what “success” constitutes, or the like. Even if such isdisplayed as part of the program selection, the initial guidance may beemployed to reiterate to the user what is expected, e.g., with regardsto intensity level, heart rate, and so on.

Data may then be tracked (step 508). The tracked data may include, e.g.,activity data (via accelerometer or GPS or other systems noted above),calorie data, meal data, analyte data such as glucose, lactate, orlactic acid, metabolism data, heart rate data, as well as other types ofdata, and combinations of the above. While certain types of metabolicand other analyte monitors including sensing systems are described ingreater detail below, here it is noted that a lactate sensor may beadvantageously employed in many implementations to monitor metabolism bycalculating a rate of energy expenditure. Appropriate analysis oflactate data may further be employed to separate energy generated fromlipid metabolism (fat) versus carbohydrate metabolism (glucose). Detailsof such distinctions are also described with respect to specificexamples below.

A next step is that the data may be analyzed (step 512). In someimplementations this step is optional, but the same may be performed to,e.g., perform analyses on the data so as to obtain related data, e.g.,to analyze GPS data to determine “miles ran”, and to analyze “miles ran”data to determine “calories burned” data, to analyze “calories burned”data to determine “fat burned” data, and so on.

The data is then evaluated against the selected program (step 514). Forexample, the data may indicate whether the intensity level desired to beachieved as a goal in the program was in fact met by the actual datarecorded about intensity during the duration of the program. As anotherexample, a program for sports optimization may desire that exercise beemployed to help mediate and control the patient's glucose level, andthe evaluation may include determining if the patient's glucose levelwas sufficiently controlled by the exercise program.

An output may then be displayed responsive to the evaluation (step 516).The outputs may be any of those noted above with respect to FIGS. 4-36,and it is reiterated that the same may be shown via a simple UI. Forexample, for sports optimization, the same may display one color if theuser is maintaining a certain level of lactic acid, heart rate,hydration, or the like.

A system may be provided as illustrated in FIG. 46. In particular, FIG.46 shows a system 550 in which a device 518 running an application 523is coupled to a sensor 524 through a transmitter 526.

In one implementation, the sensor may include an optional applicator, atransmitter mount with an adhesive pad, and a sensor probe. The sensormay be inserted into the abdominal subcutaneous tissue using, e.g., a26-gauge introducer needle encased in the applicator. The sensor probemay be a wire electrode that couples the enzyme glucose oxidase to anelectrochemical sensor electrode. The sensor generates an electricalcurrent proportional to the ambient analyte concentration in theinterstitial fluid of the subcutaneous tissue surrounding the sensor.The sensor may be held in place by a housing that is adhered to the skinusing a standard medical grade adhesive. In one implementation, thesports sensor communicates wirelessly to the sports receiver via thetransmitter at a 2.4 GHz frequency.

The transmitter may send the measured electrical analyte signalwirelessly to the device 518 at intermittent intervals. The transmittermay be programmed with a specific identification serial number that isalso programmed into the corresponding device in order to establish asecure wireless communication link between the two hardware components.The transmitter may be reusable and may be employed for repeatedsessions by a single subject user, up to the lifetime of the batteryencased in the device. The device 518 may be a mobile device including atablet, a smart phone, smart watch, or in some cases a dedicatedreceiver. The device 518 may perform signal processing algorithmsrequired to convert the sensor electrical signal to values that can bedisplayed to the user.

In use, the sensor 524 may employ the transmitter 526 to transmit dataand other signals to the device 518. In some cases, the sensor 524 willbe integral with the transmitter 526. Multiple sensors 524 may couple tomultiple transmitters 526 for subsequent transmission, or onetransmitter may service two or more sensors 524. The device 518 includesa display 522 in which a user interface provides information to a user,e.g., such as the UI 26 of the monitoring device 21 (see FIG. 2). Thedisplay 522 may be of an easy-to-understand design, and may incorporatezones, e.g., colored zones, indicating various values of thephysiological quantity to be monitored, and an arrow pointing at thezone in which the parameter is currently situated. As above, suchdisplays may be for intensity levels, lactate levels, heart rate, or thelike. In some cases, the tachometer-type outputs of FIG. 15 and FIG. 16may be of particular use in displaying this type of data. Otherdisplayed outputs as have been described may also be employed.

In one implementation, the device 518 and in particular the application523 may graphically display whether the user is in zone (which can beuser-definable) in a simple graphical color display (green) as well asdisplaying a current value of the monitored analyte, e.g., glucose. Theapplication 523 may cause the display of zones corresponding to“out-of-zone low” (red) or “out-of-zone high” (yellow or orange).Further information may be created by post-workout retrospectiveanalysis of data with potential further pattern recognition systems. Thepost-usage analysis may allow for further user-driven refinements of thedisplayed zones.

Other variations of systems for sports optimization will also beunderstood given this teaching.

FIG. 47 is a flowchart 560 illustrating an exemplary implementation ofsports optimization. In this implementation, lactate (as well aspotential other analytes) is monitored as an indicator of a “bodytachometer”. Other data may be monitored as well, including activitydata so as to determine an intensity level, as well as heart rate data,and so on. In this implementation, comparing heart rate data to lactatedata is used as an indicator of fitness level.

In particular, lactate is a byproduct of glucose consumption and becomeselevated during high intensity exercise. A high lactate level indicatespending exhaustion and loss of energy. Athletes often use lactate levelsto optimize training, but their use is limited because they requireblood draws and analysis equipment. Heart rate monitors have been usedas a surrogate for lactate, but the same are indirect. In contrast,lactate is a direct measure of the body's exercise capacity, and in somedefinitions, a user's fitness level is directly related to a combinationof their energy expenditure and their lactate threshold.

Put another way, and referring to the graph 570 of FIG. 48, a user'slactate threshold is a direct indicator of their level of fitness. Thedata set 546 indicates a lactate curve prior to a training regimen, andthe data set 548 indicates a lactate curve subsequent to a trainingregimen. As can be seen, the more fit a user is, the higher the workoutintensity must be before reaching the lactate threshold. In lowintensity workouts, lactate levels generally stay stable, while inhigh-intensity ones, lactate builds up, causing fatigue. Peakperformance is achieved when the user stays at but below this lactatethreshold.

Referring next to the graph 580 of FIG. 49, data sets are shown for acycling user. The data set 552 corresponds to heart rate, quantified onthe left vertical axis, and data set 554 corresponds to a blood lactatelevel, quantified on the right vertical axis. The aerobic threshold 551is indicated, as well as the lactate threshold 553. As noted, peakperformance is achieved when the user stays at but below the lactatethreshold.

Consequently, and returning to FIG. 47, a program (step 528) may includean attempt to perform exercise in such a way that the user's lactatelevel is a certain percentage, e.g., 80-98%, of their lactate thresholdlevel. Data may then be tracked, particularly lactate data, andoptionally other data such as activity data, meal data, heart rate data,and so on (step 536). The tracked data may then be evaluated against theselected program. For example, the data may be evaluated to determine ifthe lactate level reached the desired percentage. An output may then bedisplayed responsive to the evaluation (step 544). And the displayedoutput may correspond to any of the outputs discussed above inconnection with FIGS. 4-36.

FIG. 50 illustrates another implementation of applications of lactatemonitoring. In particular, and which is described in greater detailbelow with reference to FIG. 51-53, monitoring lactate may be employedto detect regimes in which fat burning may be optimized. In particular,lactate inhibits the breakdown of fat, but an initial rise of lactatelevels indicates an intensity known to maximize fat burn (fat is burnedat lower intensity exercise (see curve 556), while carbohydrates areburned at higher intensity exercise (see curve 558)). Thus, referring toFIG. 50, the crossover point 562, where the lactate level 558 is seen tosignificantly rise, may be employed to detect the level at which fatburn is maximized.

In another more general example, and referring again to FIG. 47, a typeII diabetic user may desire to optimize their fitness routine but withan eye toward helping control glucose levels as well. In this case, aprogram may be initiated (step 528) that is a combination of exerciseroutines, food data, activity data, and at least two monitored analytesincluding lactate and glucose.

Data may then be tracked (step 536), e.g., with lactate and glucose databeing monitored by one or two sensors, meal data tracked by user entry,and activity data tracked by the user's smart phone accelerometer. Thetracked data may then be evaluated against the selected program (step542), and an output displayed responsive to the evaluation (step 544).

If multiple variables are changing, the evaluation may result inambiguities. In some cases, the computing environment may perform a stepof disambiguation by asking the user to maintain one or more variablesconstant. For example, if it is not known whether an intense exerciseworkout or a lack of food led to a hypoglycemic event, the user may beprompted to perform similar actions but to include a larger meal or amilder workout. Other variations will be understood given thisdisclosure.

EXERCISE OPTIMIZATION EXAMPLE

For sports or exercise optimization, whether for diabetics ornon-diabetics, exemplary inputs may include those from a lactate sensor,a movement/motion sensor such as an accelerometer or GPS device, andoptionally a glucose sensor. Other data may also be entered, includingweight, age, and the like. The lactate and glucose sensors may beindwelling sensors as described elsewhere, the accelerometer may be partof a mobile device such as a smart phone, smart watch, or may form partof a wearable analyte sensor, GPS may be provided from a smart phone orsmart watch, and other data may be entered manually, retrieved from aprofile, or via other similar sensors. If the user is a CGM useralready, e.g., is a diabetic using CGM, the same CGM may be employed forexercise optimization. Otherwise, the user can be fitted with a new CGM,or may use other glucose sensing techniques. The accelerometer or motiondetector could be incorporated into the body-worn sensor or be part of aseparate sensor such as a smart watch, a smart phone, or other sensingdevice. The devices may be connected wirelessly. A user may also useheart rate as a surrogate for lactate level. However, once a certainlevel of stamina has been reached, a recalibration of heart rate versuslactate level generally is required.

The lactate sensor may be calibrated using the glucose sensor in themanner described below, or using other calibration techniques described.For example, one or more of the lactate sensor calibration parametersmay be keyed to one or more of those of the glucose sensor.

If the user has previously determined a lactate curve with respect toenergy expenditure, the same may be used to compare a current level ofenergy expenditure by the user, and the user may tune their workout tostay in a desired range. For example, a program may be started where auser is instructed to reach a certain heart rate or achieve a certainlactate value. After following the program, results can be evaluated andthe program can be modified to iteratively move the user closer to thedesired goal.

OTHER EXERCISE OPTIMIZATION EXAMPLES

Examples are provided above for exercise and sports optimization, suchthat a user may be enabled to stay in a preferred “zone” of athleticperformance without deleteriously reaching a lactate threshold. Indeed,users may be enabled to stay in any particular zone so given informationprovided by the system, and in particular a lactate sensor. Use of thelactate sensor may be in combination with that of the glucose sensor,particularly where users such as diabetics or prediabetics are usingcontinuous glucose monitoring. As noted, non-diabetics will also benefitfrom such techniques for exercise optimization. As will be described,the glucose sensor can be advantageously employed to calibrate thelactate sensor, which would otherwise be uncalibrated or would bedifficult to calibrate.

Other sensors may be employed for exercise or sports optimization,besides lactate sensors. For example, testosterone and cortisol may bemeasured for stress monitoring, determination of fatigue syndrome, andthe like. Other exemplary analytes may include epinephrine andnorepinephrine.

Examples of sensors for the monitoring of stress, both physical stressas may be encountered by high-intensity athletes, as well as emotionalstress, are described below. Such sensors include those that measurestress hormones such as testosterone and cortisol. In many cases suchsensors are electrochemical, and may be either invasive or noninvasive.For example, noninvasive sensors include those that may be strapped onthe body.

By monitoring such stress hormones along with other data, e.g., motionor accelerometer data, stress may be linked with physical activity orother markers. In this way, users can figure out common stress triggers,and determine ways to avoid stress by avoiding the stressors orperforming acts to counteract such determined triggers.

Pertinent “other data” noted above may include other analyte levels ifthe same are being monitored. In this way, stress may be correlated withcertain chemicals in the body. Where a user is taking medications orother drugs, such drug intake may also serve as an input, and the systemmay determine the effect of the same, or at least a correlation, withstress.

Other data which may be correlated include meal data, which may beentered using hash tags or any of the other techniques noted above or inthe applications incorporated by reference. After the system has learnedstress responses of the user, the user may be able to indicate that theyare eating a particular meal or performing a particular activity, andthe system can determine what the likely response will be.

In a particular example, a program may be started in which a userattempts to control their stress hormones to a desired goal level,either in general or with respect to an upcoming event. After followingthe program, results can be evaluated and the program can be modified toiteratively move the user closer to the desired goal. In this way, auser can more easily and effectively learn how to modify their stressbehavior based on their unique physiology and lifestyle patterns, toimprove their health, without the complexities and costs associated witha clinical professional. Such needs are generally individualized and arenot capable of being understood without an appropriate computingenvironment to measure the individual's response to a sufficient numberof performance impact variables.

Referring to the flowchart 600 of FIG. 51, another implementation ofpresent principles, e.g., of an application of the flowchart of FIG. 1,is for systems and methods for weight loss optimization. While specificexamples are given below, here it is noted that similar steps may befollowed as noted in prior flowcharts. A program may be selected forweight loss optimization (step 602), and in some cases a subprogram aswell (step 604). Initial guidance may be provided (step 606), suchincluding, e.g., advice on starting the program, potential recipes or acookbook as noted above with respect to element 44 of FIG. 3, or thelike.

Data may then be tracked (step 608). Similar data may be tracked asnoted with respect to FIG. 45, however, variations will be seen andcertain data given more or less priority. Exemplary data useful inweight loss optimization may include meal data, activity data (e.g., viaa smart phone accelerometer), weight data, metabolism data, lactate data(e.g., to calculate a rate of energy expenditure and to differentiateenergy generated from lipid (fat) metabolism versus carbohydrate(glucose) metabolism), heart rate data, glucose data, and combinationsof the above.

Data may be analyzed (step 612) for various purposes. For example, mealdata may be analyzed to obtain calorie data. Pictorial food data may beanalyzed to determine an identification of the foods pictured therein.Meal data may further be analyzed to determine glycemic index or otherrelated parameters. Activity data measured by an accelerometer andlactate data measured by a lactate sensor may be combined to calculatethe rate of energy expenditure, and this combination may be considered aportion of the analyze data step. Other variations will also beunderstood of the analyze data step 612.

Data may then be evaluated against the selected program (step 614). Forexample, data may be evaluated to determine if the program provides anoptimized intensity level for fat burn, and if not, an iteration mayoccur to select a new program that moves the user toward an optimizedintensity level for fat burn. Other types of evaluations will also beunderstood and are described in greater detail below with respect tospecific examples.

An output may then be displayed responsive to the evaluation (step 616).The output may be in the forms noted above with respect to FIGS. 4-36,and as noted can be limited in their complexity so as to be easilyunderstood by users. For example, textual indications may be used toprovide easily understood instructions to users. As a specific example,if a user's weight increases, but without accompanying significantglucose spikes, a textual recommendation may be displayed to the userto, e.g., lower their fat intake. Other textual recommendations mayinclude, e.g., “You are keeping good control of your glucose; now let'ssee if you can do the same within the context of a lower fat diet.Here's something to try: X, Y, and Z.” where X, Y, and Z are entries orrecipes in the cookbook.

One type of sensor of particular use in weight loss optimization schemesaccording to FIG. 51 include those that measure and report fatmetabolism rates. In particular, weight loss optimization, as well asthe sports optimization noted above, benefit from knowledge of calorieburn rates, and in particular where such knowledge is specified to thekind of calories being consumed. The weight loss optimization routinesof present principles use such knowledge and help users optimize workoutregimens and achieve their weight loss goals.

In more detail, the body uses different sources of energy depending onphysical exertion, fitness level, rate of energy expenditure, and thebody's physical characteristics. During low-to-medium rates of exertion,the body primarily uses fat reserves to supply the needed energy. Duringmore strenuous exercise, the body shifts the energy consumption from fatto carbohydrate metabolism, as noted above with respect to FIG. 50.Optimizing the rate of exertion during exercise shifts the source ofenergy consumption from carbohydrates to fat, making workouts moreeffective at eliminating fat, and thus optimizing weight loss.Additionally, where optimizing workouts includes the use of body-wornsensors as noted above with respect to FIG. 46, users may be furthermotivated by the displayed outputs to modify behavior to continuetraining regimens and achieve their goals. Thus, use of the sensorsdescribed in greater detail below may be advantageously employed to thisend.

In applying FIG. 51 to the cause of monitoring and optimizing fatconsumption, a program may be selected as indicated in FIG. 52 (step622), where in particular the selected program is related to fatconsumption. Initial guidance may be provided, e.g., specific guidancefor exercise and nutrition choices (step 624).

Data may then be tracked (step 626). In one implementation, lactatelevel and heart rate are tracked (step 636). In particular, heart ratecan be employed as a secondary measure of power output and energyexpenditure. Computing environments according to present principles canthen calculate calorie output by measuring heart rate and incorporatingother data such as body weight. As heart rate increases, more caloriesare consumed. Additionally, there is an observed correlation betweenblood lactate levels and the crossover from fat metabolism tocarbohydrate metabolism. As lactate levels increase, more carbohydratesare consumed, and less fat. It is believed that lactate can counterregulate fat consumption, making it an indicator of the shift tocarbohydrate metabolism. Thus, by taking into account heart rate andlactate levels, lipid (fat) burn rates can be calculated and optimized.

In another implementation, VO2 and VCO2 parameters and their ratio canbe determined and employed in fat burn optimization (step 638). Inparticular, the ratio of VCO2 and VO2 (VCO2/VO2 is often referred to asthe respiratory exchange ratio, “RER”, or just “R”) is different betweenfat metabolism and carbohydrate metabolism. In particular, R issubstantially 1.0 for carbohydrate metabolism or glucose metabolism, andR is substantially 0.70 for fat metabolism. The parameters are generallycalculated by measuring the concentration of oxygen and carbon dioxidethat is inhaled and exhaled, but the same can also be measured by ablood gas analyzer or within tissue using a subcutaneous sensor.

In yet another implementation, glycerol can be employed in fat burnoptimization (step 642). In particular, glycerol is a byproduct of lipidmetabolism. Direct measurement of blood or subcutaneous glycerol maythus constitute an indirect method of measuring fat metabolism. Suchmeasurements may be by way of electrochemical detection systems usingglycerol oxidase, and multi-enzyme cascades.

In yet another implementation, ketones or free fatty acids can bemeasured as an alternative type of data for measuring metabolism (step644), particularly as surrogates of fat burning. Ketones are anefficient source of fuel and energy for the human body, and are producedby the liver from fatty acids, which result from the breakdown of bodyfat in response to the absence of glucose/sugar. Electrochemicaldetection of ketones can be made via enzymes, e.g., 3-hydroxybuturatedehydrogenase, NADP dependent alcohol aldehyde/ketone oxidoreductase, orNADPH alcohol dehydrogenase.

Using steps 636, 638, 642, and/or 644, data about rates of fatmetabolism can be calculated and employed. In doing so, the data may beanalyzed (step 628), and the data may be evaluated against the selectedprogram (step 632). In particular, the data may be analyzed to determineif the measured fat consumption rate is that desired by the user, or asset as a goal in the program. An output may be displayed (step 644),such being of the forms noted above with respect to FIGS. 4-36. Forexample, a tachometer type diagram may indicate a range of fat burningor consumption, as may the use of colored ranges with a bar or needleindicator.

A system may be provided as illustrated in FIG. 53, which is similar tothe system illustrated in FIG. 46. In particular, FIG. 53 shows a system650 in which a device 652 is coupled to a lactate sensor 656 through atransmitter 658. The device 652 may be any of the types of devices notedabove with respect to device 518, and the transmitter 658 may performfunctions and be otherwise similar to the transmitter 526.

In the use of this system, it is reiterated that users generally wish tomaximize weight loss with the least amount of effort. Moreover, lowintensity exercise tends to be associated with fat consumption, whilemoderate-to-high intensity exercise is related to carbohydrateconsumption (as energy source). Accordingly, to optimize weight loss itmay be desired to display to a user, or to educate a user about, whenthey are in a preferred fat consumption zone. Optimizing an individual'srate of fat consumption will result in a faster rate of weight loss withless effort from the individual.

The metabolic crossover from fat to carbohydrate consumption is specificto individuals and also to the type of exercise being performed. Theoptimal exercise intensity for fat burn of an individual who has poorcardiovascular conditioning may be quite low, e.g., 20 to 40% of maximumintensity, while a physically-fit individual may find the optimalintensity for maximum fat burn to be significantly higher, e.g., 60 to80% of maximum intensity. In addition, rates of fat (lipid) metabolismare also highly specific to the type of exercise. Activities whichutilize more diverse muscle groups, e.g., walking or running, arebelieved to burn fat at a higher rate than activities such as cyclingwhich use more focused muscle groups. Rates of fat metabolism aretherefore highly individual and can be influenced by their exerciseintensity and type of exercise, such being related to the “exercisesensitivity” noted above.

Thus, the system 650 provides a device that can monitor an individual'smetabolic status during exercise and can notify a user of an optimalintensity range of exercise so as to optimize fat loss or cardiovasculartraining. The device can provide information on the form of energyexpenditure, and may be employed as a behavior modification device tohelp individuals optimize their daily exercise routines. Anaccelerometer within the device 652 can be employed along with a lactatesensor 656 to calculate a rate of energy expenditure and candifferentiate energy that is generated from lipid (fat) metabolismversus that generated from carbohydrate (glucose) metabolism. Theaccelerometer can be employed to measure caloric burn rate and totalcaloric consumption for exercises including walking, running, climbingstairs, and so on. Optionally, heart rate monitors may also be employed,and combining information from a heart rate monitor with a lactatesensor can be used for advanced users to improve accuracy or forexercises that cannot be accurately measured using accelerometers, e.g.,cycling, rowing, cross-country skiing, or the like.

In use of the device 650 in the method of FIG. 52, a user may select aprogram calling for a certain metabolic rate to be achieved duringexercise (step 622). Initial guidance may be provided (step 624), theinitial guidance cautioning the user, e.g., to heed displays, alerts,and warnings from the device regarding optimal intensity workouts. Forexample, the user may be instructed to work out at an intensity suchthat the crossover from fat-to-carbohydrate consumption is not reached.Data may be tracked (step 626), this data generally corresponding tometabolic rate but specified to the type of energy being consumed toperform the exercise. Data may be analyzed if necessary (step 628), andthe data evaluated (step 632) against the program selected in step 622.For example, the metabolic data may be evaluated against the goalmetabolic rate. The display may be output (step 634), generallycorresponding to success or failure of the user reaching the goal. Forexample, during use of the device 650, the displayed output maycorrespond to whether the user is in a fat-burning range or not.

Besides the above-noted weight loss optimization routines, otherexamples will also be understood. For example, one such system andmethod according to present principles may evaluate glucose with respectto weight as measured by a scale, so as to be used to calculate dietaryor exercise modifications. Analyzed data may include glucose data from aCGM and weight data from a scale, and the glucose measurement may useany of a number of calibration routines. Evaluation may includeevaluating the glucose trace with respect to the weight. In this casethe displayed output may include a modified diet plan.

Another example is that glycemic index, or glycemic impact as notedabove, may be evaluated with respect to weight. In this way, glycemicindex or impact behavior may be paired with weight as measured by ascale. Generally the same should correlate, i.e., their variabilityshould be limited, and their correlation as displayed as an output mayindicate to the user that glycemic index/impact is important in weightloss optimization.

Specific examples for optimizing weight loss are described below.Generally inputs to such systems will involve one or more selected from:weight measured by a scale, lactate levels, activity levels, glucoselevels, ketone levels, triglyceride levels, glycerol levels, caloricintake, activity level, including activity level over time, and so on.Data received may be processed and determine to either meet requirementsof a program or not, e.g., meet a threshold criteria, or not. Outputsmay span from the most basic, e.g., a numerical value relating to ananalyte being measured, to more significant and/or informative outputs,giving the user a level of insight, e.g., a zone indicator (green,yellow, or red), to actionable outputs informing a user of specificsteps to take. Even if the output is just a numerical value, the samecan indicate, e.g., rate of fat or calorie burn, rate of carbohydrateburn, percentage or ratio of fat versus calorie burn, or the like.

As with exercise optimization, outputs may be displayed via a simple UI.For example, for metabolic monitoring, the same may display one color ifthe user is burning more calories than they are consuming, and adifferent color if the opposite is true.

Weight Loss Optimization Example 1

In one example, which as for other examples may be applied tonon-diabetics as well as diabetics, a user may be instructed how tooptimize fat reduction through exercise. In this example, weight lossoptimization is performed using lactate and accelerometer measurements.

In more detail, many people exercise to lose weight and specifically tolose fat; however, most want to achieve a maximal fat loss with theleast amount of effort. A particular use case is a user who has only ahalf hour to exercise, and who desires to optimize that exercise.Optimizing the type of exercise, the intensity, and the duration, canburn fat more rapidly and more efficiently.

As noted above with respect to exercise optimization, exercise builds uplactate, and at some point as the user increases the intensity ofexertion, the body transitions from burning fat to burningcarbohydrates. For weight loss optimization, it is thus desired toexercise at a level below this crossover point.

Exemplary inputs will include those from a lactate sensor and anaccelerometer. Each of these may have surrogates. For example, thelactate sensor may be replaced with a heart rate sensor, if anappropriate correlation is known. Where a lactate sensor is used, thesame may be transcutaneous as with the continuous glucose monitorsdescribed here, or the same may be more noninvasive, e.g., using a patchfor sweat detection, or optical sensors, e.g., those operating in thenear IR. Where the lactate sensor is transcutaneous, it may be similarin structure to those described here, but where glucose oxidase isreplaced with lactate oxidase. Where heart rate is used as a surrogatefor lactate, the correlation could be incorporated into the algorithm,or alternatively heart rate could be used directly. In the same way,accelerometer data may be replaced with GPS data, particularly where theuser exercises by running, hiking, walking, biking, or the like. Theaccelerometer or other such motion detector may be incorporated into abody-worn sensor or may be part of a separate sensor such as a smartphone, smart watch, or other sensing device. The devices may beconnected wirelessly or via wired techniques.

Alternatively, the system may use a glucose sensor as a secondaryelectrode from which to calibrate, using techniques described below. Insome cases, particularly for diabetics employing “finger sticks”, afinger stick may be employed to calibrate a glucose sensor, and fromthat one may determine a lactate sensor calibration. As noted below,various ways of performing factory calibration may also be employed,negating the need for a user to take separate calibration actions.

The lactate signal and accelerometer (or other energy expendituresignal) may be displayed directly on a user interface as a value or as avalue and trend. In a more sophisticated implementation, the informationmay be employed to calculate metrics that are easier for the user tounderstand. In one case, energy expenditure information may be employedto inform a user as to how many calories are being burned, e.g., as arate, and also as a cumulative number since the start of an exercise. Asnoted above, lactate measurements give an indication of what energysource is being used to generate the energy consumed as calories. Inparticular, lactate is a by-product of carbohydrate consumption. Anoptimal fat burning exercise expends the most calories withoutsignificantly increasing the systemic lactate levels. By measuring thelactate and calorie expenditure, the percent of calories burned causedby fat reduction can be inferred and calculated. If there is lowlactate, most is being burned from fat. If there is high lactate, mostis being burned by carbohydrates. As noted above with respect to FIG.50, there is a sliding scale of fat burn ratio between a low and highlevel. The conversion factor from amount of lactate measured, and fatburn, can be set through conversion factors obtained from literature andexperiments.

Alternatively, a rate of energy expenditure may be calculated and energyexpended may then be separated into that generated from lipid (fat)metabolism versus that generated from carbohydrate (glucose) metabolism.An accelerometer (and/or heart rate monitor) could measure caloric burnrate and an integration under the curve performed to determine totalcaloric consumption for exercise.

Outputs may be displayed using user interface elements described above,and the content of the same may include rate of fat burn, which can thusbe used to optimize an exercise regimen that is intended to optimizeweight loss. The display can be of a fat or calorie burn rate, as wellas a total or cumulative value. In addition, the user interface candisplay the source of energy expended, e.g., fat versus carbohydrates.One way of displaying an individual's rate of fat consumption is toillustrate the percent being burned as fat versus the percent burned ascarbohydrates, including as a ratio. For weight loss, the user wouldgenerally want to choose a program to maximize the calorie burn rate andthe percentage of fat burn. Outputs of the program may advise the userto increase intensity or decrease intensity to achieve the desiredoutput. Optimization of weight loss generally calls for lower exertionthan that required for cardiovascular fitness or high-intensityathletics. In many cases, exercise that uses a large number of differentmuscle groups may be preferable in this regard to exercises that onlyuse a single muscle group in a concentrated way. For example, hiking orwalking may be preferable for weight loss as compared to cycling, andthis aspect may thus be seen by a user on the display using the programdescribed above.

In some cases, a number of calories consumed may be at least partiallyinferred from glucose data, and the number of calories expended may bedetermined from the lactate sensor and/or the accelerometer or otheractivity sensor. Additional details of ways of determining caloriesconsumed are described below in another example.

Thus, in a particular example, a program may be started in which a userattempts to optimize their weight loss as measured by a desired goallevel, e.g., a number of pounds per week. Inputs to the program mayinclude a lactate level and activity data. After following the program,results can be evaluated and the program can be modified to iterativelymove the user closer to the desired goal. In this way, a user can moreeasily and effectively learn how to lose weight based on their uniquephysiology and lifestyle patterns in order to improve their health,without the complexities and costs associated with a clinicalprofessional. It is further noted that such needs are generallyindividualized and are not capable of being understood without anappropriate computing environment to measure the individual's responseto a sufficient number of performance impact variables.

In one implementation, a “try and learn” approach to optimizing fat lossmay be provided as part of a program. The “self-coach” system mayrecommend types of exercises, e.g., running, swimming, walking, biking,and so on, and may further recommend various types of intensity. Theprogram may suggest different workouts to demonstrate to the user howthe different activities impact the fat burn rate. Such would indicateto the user which exercise is the best use of their time if they wish toburn fat, what exercise is the best use of their time if they wish toachieve better cardiovascular fitness, and the like. For example, thesystem and method may suggest that the user exercise in the morningmore, if such is indicated to have a particularly beneficial impact onuser health. Heart rate and accelerometer data may be compared with therate of fat burning to measure how efficient a particular exercise is atfat loss. The program may detect patterns in lactate versus intensity ofexercise, and may show progressive levels of fitness. The user goals maybe taken as inputs, and used as targets for the program.

Weight Loss Optimization Example 2

In another example, again applicable to non-diabetics and diabeticsalike, a user may be instructed how to optimize fat reduction throughmeasurement of glycerol and/or ketones or similar analytes. Glycerol,like triglycerides, is a byproduct of metabolism such as fat digestion.In this example, weight loss optimization is performed using analytesensors without necessarily measuring user activity.

In more detail, measuring rates of fat burning can help people maintainor optimize their diet. This example uses measurement of glycerol and/orketones, which are byproducts of fat metabolism, without necessarilyrequiring exercise. Thus, a user on a low carbohydrate diet could usethe system and method of this example to monitor and measure theefficacy of their approach. The fat metabolism itself may be caused byeither exercise or diet.

In particular, inputs to this system include data from measurements ofglycerol and ketones. Appropriate sensors may include two single analytesensors or a multi-analyte sensor. While either glycerol or ketones maybe employed in the present system, using both enhances accuracy as fatmay be burned along different pathways, and the use of both glycerol andketone measurements allows detection, quantification, and accounting ofmultiple of these pathways.

In some cases an array of sensors may be employed, with the most commonarray being those measuring glycerol, ketones, and potentially otherparameters such as glucose and/or triglycerides. If the glycerol andketone sensors are provided under a common enzyme layer, they may beexpected to drift in the same way. Generally, implementations mayinclude use of various sensors, including ketone sensors, glucosesensors, accelerometers, and sensors measuring glycerol, as well ascombinations of these. Particular implementations of note may include aketone sensor in isolation, a ketone and glucose sensor combinationdevice, and a ketone, glucose and accelerometer sensor combinationdevice.

Where it is additionally desired to monitor metabolism, and inparticular to determine if the same is from fat versus carbohydrates, alactate sensor as described above may also be employed in the system.

The signal received may be a direct product of the byproduct ofmetabolism, and may be used to determine that fat metabolism isoccurring. In some cases the signal may require an algorithm to correctfor background signals, calibration issues, or noise. The signal may beinterpreted by an algorithm to convert the data into a measurement offat metabolism. If two sensors are used, e.g., for glycerol and ketones,the information for each individual sensor can be used together toprovide additional information on efficacy of fat burning or as aquality check to have more accurate or reliable information. Analgorithm may also be employed to calculate cumulative fat burn overtime, as well as daily or weekly statistics.

As in the prior example, a “self-coach” system may be employed which insome cases may be combined with an interactive weight management system.In both cases, programs for exercise and meals may be suggested for auser. Specific meals may be consumed in different amounts and theresults evaluated. The individual, using the program, may learn aboutthe impact of different foods and/or exercise and be motivated by animmediate feedback for a suggested lifestyle adjustment.

As above, if glucose measurements are also considered, information maybe determined or inferred such as calories ingested, calories expended,calories left on board, as well as calculations of calories in excess ordeficit. Historical data may be employed, and past weight gain or losscompared to current values of the same.

Various ketones may be employed, including acetone, acetoacetic acid,and beta-hydroxybutyric acid.

Thus, in a particular example, a program may be started in which a userattempts to optimize their weight loss as measured by a desired goallevel, e.g., a number of pounds per week. Inputs to the program mayinclude measured glycerol and ketone levels, including levels over time.In more sophisticated implementations, glucose data may also beconsidered. After following the program, results can be evaluated andthe program can be modified to iteratively move the user closer to thedesired goal. In this way, a user can more easily and effectively learnhow to lose weight based on their unique physiology and lifestylepatterns in order to improve their health, without the complexities andcosts associated with a clinical professional. It is further noted thatsuch needs are generally individualized and are not capable of beingunderstood without an appropriate computing environment to measure theindividual's response to a sufficient number of performance impactvariables.

Weight Loss Optimization Example 3

In yet another example, again applicable to diabetics and non-diabeticsalike, a user may be instructed how to optimize fat reduction throughmeasurement of triglycerides or similar analytes. Such instructions maybe calculated or determinable by a number of factors, includingmeasurements of fat storage and fat utilization. Fat storage ismeasurable or determinable by various effects including high glucose,high insulin, high triglycerides, low glucagon, low ketones, or low freefatty aides Fat utilization is measurable or determinable by variouseffects including stable glucose, low insulin, low triglycerides, highglucagon, high ketones, and/or high free fatty acids. By measuring andtracking data about these parameters, users may be instructed tooptimize fat reduction by suggestion of meals, exercise, and othertherapies that emphasize fat utilization and lead to the effects notedabove. In this example, weight loss optimization is performed usinganalyte sensors without necessarily measurement of user activity. Thisexample particularly focuses on user eating habits, and optimizinghealthy eating.

In more detail, many people would like real time feedback on how theireating habits impact their health. This system may provide informationas to how their diet and exercise levels impact their triglyceridelevels, which correlates to the amount of excess food eaten. Inparticular, triglycerides are created when extra food is eaten that isnot being used in energy expenditure. Such “extra calories” areconverted to triglycerides prior to being stored as long term fat.Real-time monitoring of triglycerides could give valuable informationabout eating and exercise habits toward health and weight loss. Forexample, such information may include whether a user is thinking theyare eating well, by eating a salad, but in fact is sabotaging their ownefforts by adding too much salad dressing. Accordingly, by measuringtriglycerides, a user may be informed as to how much fat they areingesting.

In this implementation, a resting metabolism may be a useful parameterin calculations, as the same relates to how much energy will be consumedby the user even in the absence of exercise or excess energyexpenditure. Resting metabolism may be determined in a number of ways,e.g., using CO2 breath levels as measured in a chamber with known gases,by knowledge of a basal level of lactate production, and the like.

In particular, one input to the system may include a measurement oftriglycerides, either in the bloodstream or subcutaneous, or both. Insome cases, a level of lipoproteins, which serve to transport lipidssuch as triglycerides, may be measured. For subcutaneous measurements, asystem similar to those described here may be employed, with anappropriate enzyme exchange, e.g., using lipase, which breakstriglycerides into glycerol and three free fatty acids. Noninvasivetechniques may also be employed

In some cases an array of sensors may be employed, with the most commonarray being those measuring triglycerides and potentially anotheranalyte such as glucose. If the triglycerides and glucose sensors areprovided under a common enzyme layer, they may be expected to drift inthe same way.

In some cases the signal received may require an algorithm to correctfor background signals, calibration issues, or noise. The signal may beinterpreted by an algorithm to convert the data into a metric thatcompares healthy levels to unhealthy levels, rather than against aparticular concentration. For example, comparison may be made against atypically acceptable level, in some cases calculating a difference fromthe normal, and expressed as a number such as a dietary health quotient.Alternatively, a zone diagram may be displayed with, e.g., yellow, red,and green zones. Other helpful metrics may include averages over time,area under the curve, cumulative levels over time, and the like.

As in the prior example, a “self-coaching” system may be employed.Programs for exercise and meals may be suggested for users, such asusing recommendations in a cookbook or eating program. Specific mealsmay be consumed in different amounts and the results evaluatedimmediately. The individual, using the program, may learn about theimpact of different foods and/or exercise and be motivated by animmediate feedback for a suggested lifestyle adjustment. For example, auser may be alerted to excess fat in their diet, and may be suggested tolower their fat intake. In the same way, particularly if glucose ismeasured at the same time, a user may be alerted to excess sugar intheir diet, and may be directed to lower their sugar intake.

In more detail, it has been hypothesized that glucose levels in responseto food intake differ with respect to the type of food. If carbohydratesare ingested, the glucose levels follow a normal curve, while if fatsare ingested, a normal curve is again seen but with a significantelevation or “tail” on the right side. This aspect may be employed inseveral of the examples to determine if ingested food containsignificant levels of fats or carbohydrates.

It is noted here that triglycerides are a particular type of fat, butmultiple types of fat may be measured and employed in systems andmethods according to present principles.

The system may be further employed to indicate to a user how healthytheir meals are. Using data such as a measurement of free fatty acids,caloric intake, as well as types of calories, the system in this examplemay indicate to a user how their diet and lifestyle is stressing theirbody, e.g., in insulin production, glucose effects, or the like.

Thus, in a particular example, a program may be started in which a userattempts to optimize their weight loss as measured by a desired goallevel, e.g., a number of pounds per week. Inputs to the program mayinclude measured triglyceride and optionally glucose levels, includinglevels over time. After following the program, results can be evaluatedand the program can be modified to iteratively move the user closer tothe desired goal. In this way, a user can more easily and effectivelylearn how to lose weight based on their unique physiology and lifestylepatterns in order to improve their health, without the complexities andcosts associated with a clinical professional. Such needs are generallyindividualized and are not capable of being understood without anappropriate computing environment to measure the individual's responseto a sufficient number of performance impact variables.

Weight Loss Optimization Example 4

In yet a further generally applicable example, a user may be enabled todetermine their calories ingested and their calories expended, in manycases termed “calories—calories out”. In particular, one potentialapplication for a real-time monitoring system is to measure the amountof calories taken in by the body. Such systems may be combined withthose measuring calories expended to give a real-time indicator as towhether a user is losing weight or gaining it.

The determination of calories expended may be made in a number of ways.In some cases, knowledge of activity, combined with knowledge of auser's weight, fitness level, or the like, may be used to estimatecalories expended during exercise. Other ways of determining caloriesexpended, or to refine calculations using other methods, is bymeasurement of lactate as noted above with respect to exercise andsports optimization.

The determination of calories ingested may in a simplest sense bedetermined by glucose, such as may be determined by a CGM system.Related indices include total daily glucose, total meal glucose, or thelike, and the same can generally be obtained by numerical integrationunder a glucose value curve. A derivative of the glucose value curve cangive another type of indicator, in particular, a glucose rate indicator,which can be of particular value after exercise in determining andcharacterizing glucose drops. The rate of change of the analyte, thevalue of the analyte concentration, or the cumulative total may becharacterized as being above normal, below normal, or at normal, and thesame may be characterized as such using the exemplary user interfacesdescribed above.

In a more sophisticated calculation, insulin may also be measured andused in combination with glucose data. Alternatively, insulin analoguesor surrogates may be used. Measuring insulin together with glucoseallows the determination of insulin action, the ability of insulin toenhance glucose uptake and simultaneously suppress endogenous glucoseproduction. In this way, the combination of glucose measurements andinsulin measurements can allow a relatively complete picture to beobtained of a person's metabolic health in relation to caloriesingested, i.e., caloric uptake.

In an even more sophisticated and accurate calculation, triglyceridemeasurements may be employed to determine the amount of fat ingested bya user. Methods of triglyceride measurements are discussed above.

The type of sensors which may be employed for the above measurements areas follows. Glucose measurements may be taken using CGM systems orothers described here. Insulin measurements are generally moredifficult, as insulin is present in only low quantities, but highsensitivity measurements may be employed to measure the same.

In one implementation, an array of sensors is employed to measureinsulin, glucose, and triglycerides. An accelerometer or other motionsensor may be employed to detect user activity. Using the array sensorand the accelerometer, a determination may be made as to total caloriesingested and total calories expended, thus giving an indication as towhether the user is losing or gaining weight.

In addition, insulin measurements may be employed to determineprogression of disease state. As diabetes progresses, insulin becomesless and less effective, and more and more must accordingly be createdand used by the body. If in the course of following programsprogressively more insulin becomes required, with relatively equalamounts of food ingested and exercise performed, an inference can bemade that insulin is becoming less and less effective. In addition,besides use in determining calories ingested, insulin measurements maybe used as part of an “insulin-on-board” calculation for use in diabetesmanagement.

In this example, as in the one above, a resting metabolism may be auseful parameter in calculations, as the same relates to how much energywill be consumed by the user even in the absence of exercise or excessenergy expenditure. Resting metabolism may be determined in a number ofways, e.g., using CO₂ breath levels as measured in a chamber with knowngases, by knowledge of a basal level of lactate production, and thelike.

In some cases the insulin signal received may require an algorithm tocorrect for background signals, calibration issues, or noise. The signalmay be interpreted by an algorithm to convert the data into a metricthat indicates weight gain or weight loss.

As in the prior example, a “self-coaching” system may be employed.Programs for exercise and meals may be suggested for users, such asusing recommendations in a cookbook or eating program. Specific mealsmay be consumed in different amounts and the results evaluated. Theindividual, using the program, may learn about the impact of differentfoods and/or exercise and may be motivated by an immediate feedback fora suggested lifestyle adjustment. For example, a user may be alerted toexcess fat in their diet, and may be suggested to lower their fatintake. In the same way, a user may be alerted to excess sugar in theirdiet, and may be suggested to lower their sugar intake.

Exemplary outputs for the user may be estimated calories input or outputversus time, as well as a calculated area under the curve, i.e., totalcalories consumed or expended, for various time points. One exemplaryindicator may be total calories burned over time, showing how muchexcess (weight gain) or deficit (weight loss) was happening over time.Another output may be an indicator of how much fat is being burned, thisoutput in many cases using a measurement of lactate, glucose, heartrate, accelerometer, or other calorie-burning measures.

Thus, in a particular example, a program may be started in which a userattempts to optimize their weight loss as measured by a desired goallevel, e.g., a certain number of calories expended which is greater thanthe number of calories ingested. Inputs to the program may includemeasured insulin, glucose, and in some cases triglyceride levels,including levels over time. After following the program, results can beevaluated and the program can be modified to iteratively move the usercloser to the desired goal. In this way, a user can more easily andeffectively learn how to lose weight based on their unique physiologyand lifestyle patterns in order to improve their health, without thecomplexities and costs associated with a clinical professional.

As a specific example, users may be informed as to the best times toexercise or eat meals to maximize calorie expenditure, or the best timesto exercise or eat meals with regard to insulin production. Users canthus condition their bodies to use the insulin they produce in a moreeffective way. Prediabetics may be enabled to determine their diabeticstate and to reverse a trend toward diabetes. For example, bymaintaining or losing weight while producing the same or less amounts ofinsulin, a trend toward diabetes, as measured by pancreas function, maybe halted or even reversed.

It is further noted that such needs are generally individualized and arenot capable of being understood without an appropriate computingenvironment to measure the individual's response to a sufficient numberof performance impact variables. Moreover, in this and in otherexamples, measurements within the interstitial space of the indicatedanalytes provides a data collection regime that is both highly accurateand prolific in that significant quantities of data may be obtained foranalysis.

As noted above, systems and methods according to present principlesprovide ways for users to employ analyte monitors and other sensors aswell as control systems for self-education about a disease state or foravoidance of a disease state, as well as to enable a user's education inthe optimization of a sports or weight loss regimen. In the specificfield of diabetes management, such can include the education of type IIdiabetic users to better manage their disease. These users, in contrastto type I diabetic users, are in many cases unaccustomed to complicatedand/or repetitive calibration routines for continuous analyte (glucose)monitors. Accordingly, for an ideal system and method for the type IIdiabetic user, as well as for those interested in optimizing theirsports and/or weight loss regimens, it is desirable to provide sensorsand calibration routines that do not require significant user technicalsavvy to operate, and that do not require complicated calibrationroutines. It should be understood, however, that in some cases,particularly for current type I users of CGM systems, current sensortechnology and monitoring means may be employed to accomplish themethods and goals described above. In most implementations, anycommercially available sensor of current design may be used.

Systems and methods according to present principles may beadvantageously used by both diabetics and non-diabetics alike, as notedabove. In particular, any of the examples described, whether for sports,exercise, or fitness optimization, or for weight loss, or for otherpurposes, may be used by non-diabetics, diabetics, and other users aswell.

In some cases, sensors not requiring significant calibration steps maybe employed because the accuracy requirements of the sensors are oftenlessened in such implementations. Lessened accuracy and resolutionrequirements may also allow the use of less expensive sensors. In otherwords, so long as the sensor is accurate to a given tolerance, it may beemployed in such implementations, as the implementation may only requireaccuracy within a given range. For example, it may only be necessary toknow whether the patient is hypoglycemic, hyperglycemic, or euglycemic,rather than a degree of hypoglycemia or hyperglycemia. Using rangecolors as noted above with respect to, e.g., FIGS. 7, 8, 9, 12, 13, 14,15, 16, 46, and 53, it may only be required to know if a user's glucoseconcentration value is within a red/green/yellow zone, or between, e.g.,100 and 200, rather than requiring knowledge of the exact value. Whereasthe primary need for a type I patient is accurate glucose information inorder to determine trends in glucose, actual glucose levels, and/orpredicted glucose states, the broader type II population may benefitfrom a wider variety of biological information, even at lowerresolutions of accuracy, so long as the information is presented in asimple, intuitive, and actionable manner.

Additional details about providing analyte concentration values inranges rather than strictly in numerical values may be found in U.S.Pat. Publ. No. 2014/0278189-A1, and in U.S. Provisional Application No.62/053,733, filed Sep. 22, 2014, each of which is incorporated byreference herein in its entirety.

One type of sensor system which may be employed is described in U.S.Publ. No. 2009/0076360-A1, incorporated by reference herein in itsentirety.

In some cases, users may find current insertion techniques for CGMsensors to be too complex or painful. In such cases simpler and lesspainful options may be employed, such as are described in U.S. Pat.Publ. No. 2009/0076360-A1, U.S. Pat. Publ. No. 2011/0077490-A1, U.S.Pat. Publ. No. 2014/0107450-A1, and U.S. Pat. Publ. No. 2014/0213866-A1,each of which is incorporated by reference herein in its entirety.

In some cases, the system may be made even simpler by provision of apower source on board, thus reducing the overall number of components inhelping the cause of miniaturization. Certain aspects regardingimplementing power on board are described in U.S. Pat. Publ. No.2009/0076360-A1, incorporated herein by reference in its entirety.

It is also noted that power may be conserved by transmitting data on an“on demand” basis, i.e., only when the user indicates a desire to viewthe measured data. Provision may still be had for device-initiatedtransmissions, such as where a glycemic urgency is indicated, e.g., ahypoglycemic state, and in these cases data may be pushed to a nearestconnected device and from there to other resources on the internet. Forexample, data may be pushed to a local network via a Wi-Fi hotspot, ormay be pushed to a telecommunications network, e.g., either directly orthrough a user's cell phone, and subsequently transmitted to a caregiveror other monitor.

Additional details about data transmissions in such cases are describedin U.S. Pat. Publ. No. 2014/0118138-A1; U.S. Provisional PatentApplication No. 61/978,151, filed Apr. 10, 2014; U.S. patent applicationSer. No. 14/659,263, filed Mar. 16, 2015; and U.S. Provisional Appl. No.62/053,733, filed Sep. 22, 2014, each of which is incorporated herein byreference in its entirety.

Other techniques may also be employed to simplify sensor systems, andthereby make such sensors more appropriate or accessible for moregeneral use.

For example, an exemplary sensor system is described below with respectto FIGS. 54-57.

U.S. Pat. Publ. No. 2011/002712-A1, U.S. Pat. Publ. No. 2008/0119703-A1and U.S. Pat. Publ. No. 2005/0245799-A1, each of which is incorporatedherein by reference in its entirety, describe additional configurationsfor using the continuous sensor in different body locations. In someembodiments, the sensor is configured for transcutaneous implantation inthe host. In alternative embodiments, the sensor is configured forinsertion into the circulatory system, such as a peripheral vein orartery. However, in other embodiments, the sensor is configured forinsertion into the central circulatory system, such as but not limitedto the vena cava. In still other embodiments, the sensor can be placedin an extracorporeal circulation system, such as but not limited to anintravascular access device providing extracorporeal access to a bloodvessel, an intravenous fluid infusion system, an extracorporeal bloodchemistry analysis device, a dialysis machine, a heart-lung machine(i.e., a device used to provide blood circulation and oxygenation whilethe heart is stopped during heart surgery), etc. In still otherembodiments, the sensor can be configured to be wholly implantable, asdescribed in U.S. Pat. No. 6,001,067.

FIGS. 54 through 57 illustrate an embodiment of the in vivo portion of acontinuous analyte sensor 700, which includes an elongated conductivebody 702. The elongated conductive body 702 includes a core 710 (seeFIG. 55) and a first layer 712 at least partially surrounding the core.The first layer includes a working electrode (for example, located inwindow 706) and a membrane 708 located over the working electrode. Insome embodiments, the core and first layer can be of a single material(such as, for example, platinum). In some embodiments, the elongatedconductive body is a composite of at least two materials, such as acomposite of two conductive materials, or a composite of at least oneconductive material and at least one non-conductive material. In someembodiments, the elongated conductive body comprises a plurality oflayers. In certain embodiments, there are at least two concentric orannular layers, such as a core formed of a first material and a firstlayer formed of a second material. However, additional layers can beincluded in some embodiments. In some embodiments, the layers arecoaxial.

The elongated conductive body may be long and thin, yet flexible andstrong. For example, in some embodiments, the smallest dimension of theelongated conductive body is less than about 0.1 inches, 0.075 inches,0.05 inches, 0.025 inches, 0.01 inches, 0.004 inches, or 0.002 inches.While the elongated conductive body is illustrated in FIGS. 54 through57 as having a circular cross-section, in other embodiments thecross-section of the elongated conductive body can be ovoid,rectangular, triangular, polyhedral, star-shaped, C-shaped, T-shaped,X-shaped, Y-shaped, irregular, or the like. In one embodiment, aconductive wire electrode is employed as a core. To such a cladelectrode, two additional conducting layers may be added (e.g., withintervening insulating layers provided for electrical isolation). Theconductive layers can be comprised of any suitable material. In certainembodiments, it can be desirable to employ a conductive layer comprisingconductive particles (i.e., particles of a conductive material) in apolymer or other binder.

The materials used to form the elongated conductive body (such as, forexample, stainless steel, titanium, tantalum, platinum,platinum-iridium, iridium, certain polymers, alloys or combinationsthereof, and/or the like) can be strong and hard, and therefore areresistant to breakage. In some embodiments, the sensor's small diameterprovides flexibility to these materials, and therefore to the sensor asa whole. Thus, the sensor can withstand repeated forces applied to it bysurrounding tissue.

In addition to providing structural support, resiliency and flexibility,in some embodiments, the core 710, or a component thereof, provideselectrical conduction for an electrical signal from the workingelectrode to sensor electronics (not shown). In some embodiments, thecore 710 comprises a conductive material, such as stainless steel,titanium, tantalum, a conductive polymer, and/or the like. However, inother embodiments, the core is formed from a non-conductive material,such as a non-conductive polymer. In yet other embodiments, the corecomprises a plurality of layers of materials. For example, in oneembodiment the core includes an inner core and an outer core. In afurther embodiment, the inner core is formed of a first conductivematerial and the outer core is formed of a second conductive material.For example, in some embodiments, the first conductive material isstainless steel, titanium, tantalum, a conductive polymer, an alloy,and/or the like, and the second conductive material is a conductivematerial selected to provide electrical conduction between the core andthe first layer, and/or to attach the first layer to the core (that is,if the first layer is formed of a material that does not attach well tothe core material). In another embodiment, the core is formed of anon-conductive material (such as, for example, a non-conductive metaland/or a non-conductive polymer) and the first layer is formed of aconductive material, such as stainless steel, titanium, tantalum, aconductive polymer, and/or the like. The core and the first layer can beof a single (or same) material, such as platinum. One skilled in the artappreciates that additional configurations are possible.

Referring again to FIGS. 54-57, the first layer 712 can be formed of aconductive material and the working electrode can be an exposed portionof the surface of the first layer 712. Accordingly, the first layer 712can be formed of a material configured to provide a suitableelectroactive surface for the working electrode, a material such as, butnot limited to, platinum, platinum-iridium, gold, palladium, iridium,graphite, carbon, a conductive polymer, an alloy and/or the like.

As illustrated in FIGS. 55-56, a second layer 704 surrounds at least aportion of the first layer 712, thereby defining the boundaries of theworking electrode. In some embodiments, the second layer 704 serves asan insulator and is formed of an insulating material, such as polyimide,polyurethane, parylene, or any other known insulating materials. Forexample, in one embodiment the second layer is disposed on the firstlayer and configured such that the working electrode is exposed viawindow 706. In some embodiments, an elongated conductive body, includingthe core, the first layer and the second layer, is provided. A portionof the second layer can be removed to form a window 706, through whichthe electroactive surface of the working electrode (that is, the exposedsurface of the first layer 712) is exposed. In some embodiments, aportion of the second and (optionally) third layers can be removed toform the window 706, thus exposing the working electrode. Removal ofcoating materials from one or more layers of the elongated conductivebody (for example, to expose the electroactive surface of the workingelectrode) can be performed by hand, excimer lasing, chemical etching,laser ablation, grit-blasting, or the like.

The sensor can further comprise a third layer 714 comprising aconductive material. For example, the third layer 714 may comprise areference electrode, which may be formed of a silver-containing materialthat is applied onto the second layer 704 (that is, the insulator).

The elongated conductive body 702 can further comprise one or moreintermediate layers (not shown) located between the core 710 and thefirst layer 712. For example, the intermediate layer can be one or moreof an insulator, a conductor, a polymer, and/or an adhesive.

It is contemplated that the ratio between the thickness of thesilver/silver chloride layer and the thickness of an insulator (such as,for example, polyurethane or polyimide) layer can be controlled, so asto allow for a certain error margin (that is, an error margin associatedwith the etching process) that would not result in a defective sensor(for example, due to a defect resulting from an etching process thatcuts into a depth more than intended, thereby unintentionally exposingan electroactive surface). This ratio may be different depending on thetype of etching process used, whether it is laser ablation, gritblasting, chemical etching, or some other etching method. In oneembodiment in which laser ablation is performed to remove asilver/silver chloride layer and a polyurethane layer, the ratio of thethickness of the silver/silver chloride layer and the thickness of thepolyurethane layer can be from about 1:5 to about 1:1, or from about 1:3to about 1:2.

In some embodiments, the core 710 comprises a non-conductive polymer andthe first layer 712 comprises a conductive material. Such a sensorconfiguration can advantageously provide reduced material costs, in thatit replaces a typically expensive material with an inexpensive material.For example, the core 710 can be formed of a non-conductive polymer,such as, a nylon or polyester filament, string or cord, which can becoated and/or plated with a conductive material, such as platinum,platinum-iridium, gold, palladium, iridium, graphite, carbon, aconductive polymer, and allows or combinations thereof.

As illustrated in FIGS. 56 and 57, the sensor can also include amembrane 708, such as those discussed elsewhere here. The membrane 708can include an enzyme layer (not shown), as described elsewhere herein.For example, the enzyme layer can include a catalyst or enzymeconfigured to react with an analyte. For example, the enzyme layer canbe an immobilized enzyme layer including glucose oxidase. In otherembodiments, the enzyme layer can be impregnated with other oxidases,including, for example, galactose oxidase, cholesterol oxidase, aminoacid oxidase, alcohol oxidase, lactate oxidase, or uricase.

FIG. 55 is a schematic illustrating an embodiment of an elongatedconductive body 702, or elongated body, wherein the elongated conductivebody is formed from at least two materials and/or layers of conductivematerial. The term “electrode” can be used herein to refer to theelongated conductive body, which includes the electroactive surface thatdetects the analyte. In some embodiments, the elongated conductive bodyprovides an electrical connection between the electroactive surface(that is, the working electrode) and the sensor electronics (not shown).In certain embodiments, each electrode (that is, the elongatedconductive body on which the electroactive surface is located) is formedfrom a fine wire with a diameter of from about 0.001 inches or less toabout 0.01 inches or more. Each electrode can be formed from, forexample, a plated insulator, a plated wire, or bulkelectrically-conductive material. For example, in some embodiments, thewire and/or elongated conductive body used to form a working electrodeis about 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01,0.015, 0.02, 0.025, 0.03, 0.035, 0.04 or 0.045 inches in diameter.

Furthermore, the first layer can comprise an electroactive surface (thatis, the portion exposed through the window 706). The exposedelectroactive surface can be the working electrode. For example, if thesensor is an enzymatic electrochemical analyte sensor, the analyteenzymatically reacts with an enzyme in the membrane covering at least aportion of the electroactive surface. The reaction can generateelectrons (e) that are detected at the electroactive surface as ameasurable electronic current. For example, in the detection of glucosewhere glucose oxidase produces hydrogen peroxide as a byproduct,hydrogen peroxide reacts with the surface of the working electrodeproducing two protons (2H⁺), two electrons (2e⁻) and one molecule ofoxygen (O₂), which produces the electronic current being detected.

As previously described with reference to FIG. 54 and as illustrated inFIG. 56, an insulator 704 is disposed on at least a portion of theelongated conductive body 702. In some embodiments, the sensor isconfigured and arranged such that the elongated body includes a core 710and a first layer 712, and a portion of the first layer 712 is exposedvia window 706 in the insulator 704. In other embodiments, the sensor isconfigured and arranged such that the elongated body 702 includes a core710 embedded in an insulator 704, and a portion of the core 710 isexposed via the window 706 in the insulator 704. For example, theinsulating material can be applied to the elongated body 702 (by, forexample, screen-, ink-jet and/or block-print) in a configurationdesigned to leave at least a portion of the first layer's 712 surface(or the core's 710 surface) exposed. For example, the insulatingmaterial can be printed in a pattern that does not cover a portion ofthe elongated body 702. Alternatively, a portion of the elongated body702 can be masked prior to application of the insulating material.Removal of the mask, after insulating material application, can exposethe portion of the elongated body 702.

In some embodiments, the insulating material 704 comprises a polymer,for example, a non-conductive (e.g., dielectric) polymer. Dip-coating,spray-coating, vapor-deposition, printing and/or other thin film and/orthick film coating or deposition techniques can be used to deposit theinsulating material on the elongated body 702 and/or core 710. Forexample, in some embodiments, the insulating material is applied as alayer of from about less than 5 microns, or from 5, 10 or 15-microns toabout 20, 25, 30 or 35-microns or more in thickness. The insulator canbe applied as a single layer of material, or as two or more layers,which are comprised of either the same or different materials, asdescribed elsewhere herein. Alternatively, the conductive core may notrequire a coating of insulator. In some embodiments, the insulatingmaterial defines an electroactive surface of the analyte sensor (thatis, the working electrode). For example, a surface of the conductivecore (such as, for example, a portion of the first layer 712) can eitherremain exposed during the insulator application, or a portion of appliedinsulator can be removed to expose a portion of the conductive core'ssurface, as described above.

In some embodiments, in which the sensor has an insulated elongated bodyor an insulator disposed upon a conductive structure, a portion of theinsulating material can be stripped or otherwise removed, for example,by hand, excimer lasing, chemical etching, laser ablation, grit-blasting(such as, for example, with sodium bicarbonate or other suitable grit),or the like, to expose the electroactive surfaces. In one exemplaryembodiment, grit blasting is implemented to expose the electroactivesurface(s), for example, by utilizing a grit material that issufficiently hard to ablate the polymer material yet also sufficientlysoft so as to minimize or avoid damage to the underlying metal electrode(for example, a platinum electrode). Although a variety of “grit”materials can be used (such as, for example, sand, talc, walnut shell,ground plastic, sea salt, and the like), in some embodiments, sodiumbicarbonate is an advantageous grit-material because it is sufficientlyhard to ablate, e.g., a parylene coating without damaging, e.g., anunderlying platinum conductor. An additional advantage of sodiumbicarbonate blasting includes its polishing action on the metal as itstrips the polymer layer, thereby eliminating a cleaning step that mightotherwise be necessary. Alternatively, a portion of an electrode orother conductive body can be masked prior to depositing the insulator inorder to maintain an exposed electroactive surface area.

The electroactive surface of the working electrode can be exposed byformation of a window 706 in the insulator 704. The electroactive window706 of the working electrode can be configured to measure theconcentration of an analyte.

In some embodiments, a silver wire is formed onto and/or fabricated intothe sensor and subsequently chloridized to form a silver/silver chloridereference electrode. Advantageously, chloridizing the silver wire asdescribed herein enables the manufacture of a reference electrode, withgood in vivo performance. By controlling the quantity and amount ofchloridization of the silver to form silver/silver chloride, improvedbreak-in time, stability of the reference electrode and extended lifecan be obtained in some embodiments. Additionally, use of silverchloride as described above allows for relatively inexpensive and simplemanufacture of the reference electrode.

Referring to FIGS. 55-56, the reference electrode 714 can comprise asilver-containing material (e.g., silver/silver chloride) applied overat least a portion of the insulating material 704, as discussed ingreater detail elsewhere herein. For example, the silver-containingmaterial can be applied using thin film and/or thick film techniques,such as but not limited to dipping, spraying, printing,electro-depositing, vapor deposition, spin coating, and sputterdeposition, as described elsewhere herein. For example, a silver orsilver chloride-containing paint (or similar formulation) can be appliedto a reel of the insulated conductive core. Alternatively, the reel ofinsulated elongated body (or core) may be cut into single unit pieces(that is, “singularized”), and silver-containing ink may be pad printedthereon. In still other embodiments, the silver-containing material maybe applied as a silver foil. For example, an adhesive can be applied toan insulated elongated body, around which the silver foil can then bewrapped in. Alternatively, the sensor can be rolled in Ag/AgClparticles, such that a sufficient amount of silver sticks to and/orembeds into and/or otherwise adheres to the adhesive for the particlesto function as the reference electrode. In some embodiments, thesensor's reference electrode includes a sufficient amount of chloridizedsilver that the sensor measures and/or detects the analyte for at leastthree days.

It is contemplated that the electrode may be formed to have any of avariety of cross-sectional shapes. For example, in some embodiments, theelectrode may be formed to have a circular or substantially circularcross-sectional shape, but in other embodiments, the electrode may beformed to have a cross-sectional shape that resembles an ellipse, apolygon (e.g., triangle, square, rectangle, parallelogram, trapezoid,pentagon, hexagon, octagon), or the like. In various embodiments, thecross-sectional shape of the electrode may be symmetrical, but in otherembodiments, the cross-sectional shape may be asymmetrical. In someembodiments, each electrode may be formed from a fine wire with adiameter of from about 0.001 or less to about 0.050 inches or more, forexample, and may be formed from, e.g., a plated insulator, a platedwire, or bulk electrically conductive material. In some embodiments, thewire used to form a working electrode may be about 0.002, 0.003, 0.004,0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.015, 0.02, 0.025, 0.03,0.035, 0.04, or 0.045 inches in diameter. In some embodiments, theworking electrode may comprise a wire formed from a conductive material,such as platinum, platinum-black, platinum-iridium, palladium, graphite,gold, carbon, ruthenium, rhodium, osmium, and oxides or alloys thereof,conductive polymers, or the like. Although the illustrated electrodeconfiguration and associated text describe one method of forming asensor, any of a variety of known sensor configurations can be employedwith the analyte sensor system.

In some alternative embodiments, additional electrodes can be includedwithin the assembly, for example, a three-electrode system (working,reference, and counter electrodes) and an additional working electrode(e.g., an electrode which can be used to generate oxygen, which isconfigured as a baseline subtracting electrode, or which is configuredfor measuring additional analytes). U.S. Pat. No. 7,081,195, U.S. Pat.Publ. No. 2005/0143635-A1 and U.S. Pat. Publ. No. 2007/0027385-A1describe some systems and methods for implementing and using additionalworking, counter, and reference electrodes. In one implementationwherein the sensor comprises two working electrodes, the two workingelectrodes are juxtapositioned, around which the reference electrode isdisposed (e.g., helically wound). In some embodiments wherein two ormore working electrodes are provided, the working electrodes can beformed in a double-, triple-, quad-, etc. helix configuration along thelength of the sensor (for example, surrounding a reference electrode,insulated rod, or other support structure). The resulting electrodesystem can be configured with an appropriate membrane system, whereinthe first working electrode is configured to measure a first signalcomprising glucose and baseline signals, and the additional workingelectrode is configured to measure a baseline signal consisting of thebaseline signal only. In these embodiments, the second working electrodemay be configured to be substantially similar to the first workingelectrode, but without an enzyme disposed thereon. In this way, thebaseline signal can be determined and subtracted from the first signalto generate a difference signal, i.e., a glucose-only signal that issubstantially not subject to fluctuations in the baseline or interferingspecies on the signal, such as described in U.S. Pat. Publ. No.2005/0143635-A1, U.S. Pat. Publ. No. 2007/0027385-A1, and U.S. Pat.Publ. No. 2007/0213611-A1, and U.S. Pat. Publ. No. 2008/0083617-A1.

It is contemplated that the sensing region may include any of a varietyof electrode configurations. For example, in some embodiments, inaddition to one or more glucose-measuring working electrodes, thesensing region may also include a reference electrode or otherelectrodes associated with the working electrode. In these particularembodiments, the sensing region may also include a separate reference orcounter electrode associated with one or more optional auxiliary workingelectrodes. In other embodiments, the sensing region may include aglucose-measuring working electrode, an auxiliary working electrode, twocounter electrodes (one for each working electrode), and one sharedreference electrode. In yet other embodiments, the sensing region mayinclude a glucose-measuring working electrode, an auxiliary workingelectrode, two reference electrodes, and one shared counter electrode.

It is to be understood that sensing membranes modified for othersensors, for example, may include fewer or additional layers. Forexample, in some embodiments, the membrane system may comprise oneelectrode layer, one enzyme layer, and two bioprotective layers, but inother embodiments, the membrane system may comprise one electrode layer,two enzyme layers, and one bioprotective layer. In some embodiments, thebioprotective layer may be configured to function as the diffusionresistance domain and control the flux of the analyte (e.g., glucose) tothe underlying membrane layers.

In some embodiments, one or more domains of the sensing membranes may beformed from materials such as silicone, polytetrafluoroethylene,polyethylene-co-tetrafluoroethylene, polyolefin, acrylates, poly(vinylpyridine), polyvinyl/pyrrolidone, polyester, polycarbonate, biostablepolytetrafluoroethylene, homopolymers, copolymers, terpolymers ofpolyurethanes, polypropylene (PP), polyvinylchloride (PVC),polyvinylidene fluoride (PVDF), polybutylene terephthalate (PBT),polymethylmethacrylate (PMMA), polyether ether ketone (PEEK),polyurethanes, cellulosic polymers, poly(ethylene oxide), poly(propyleneoxide), zwitterions (e.g., betaines), polyelectrolytes, polysulfones,and copolymers thereof including, for example, di-block, tri-block,alternating, random and graft copolymers, and blends thereof.

In some embodiments, the sensing membrane can be deposited on theelectroactive surfaces of the electrode material using known thin orthick film techniques (for example, spraying, electro-depositing,dipping, or the like). It should be appreciated that the sensingmembrane located over the working electrode does not have to have thesame structure as the sensing membrane located over the referenceelectrode; for example, the enzyme domain deposited over the workingelectrode does not necessarily need to be deposited over the referenceor counter electrodes.

Although the exemplary embodiments illustrated in the figures involvecircumferentially-extending membrane systems, the membranes describedherein may be applied to any planar or non-planar surface, for example,the substrate-based sensor structure of U.S. Pat. No. 6,565,509 to Sayet al.

Other details of such sensor systems are described in U.S. Pat. Publ.No. 2013/0053665-A1, incorporated herein by reference in its entirety.

In many implementations, including as described above, present glucosesensors measure a glucose surrogate such as hydrogen peroxide, which isa product of the reaction of glucose and oxygen. In particular, such aglucose conversion reaction requires oxygen as a co-reactant, and thesame must be present in equi-molar quantities to glucose for thereaction to proceed. Accordingly such sensors are limited by the amountof oxygen present. If at times the oxygen is limited, then the sensorbecomes inaccurate in its reading of glucose. In this case the sensorswill be detecting oxygen instead of glucose. In practice, this maymanifest itself as an end-of-life fault. Additional details about suchend-of-life faults are described in U.S. Provisional Application No.62/053,733, filed Sep. 22, 2014, which is incorporated herein byreference in its entirety.

The amount of glucose can be reduced by the use of the diffusionresistance domain or layer noted above, but using such a mechanismtypically lowers the overall amount of signal. Accordingly, in someimplementations it would be desirable to reduce sensor dependence onoxygen.

One solution is to have an oxygen-independent enzyme, e.g., glucosedehydrogenase, e.g., GDH, GDH-NAD, GDH-PQQ, or the like, configured tocatalyze a reaction with glucose as a reactant. However, past efforts ofusing an oxygen-independent enzyme have suffered from issues related tothe enzyme's lack of specificity to glucose, which can result in crossreactions with other sugars such as galactose or maltose, which in turnresults in a signal that includes contributions from non-glucosespecies. In some embodiments, an oxygen independent enzyme complex isused that is formed by bonding a co-factor (e.g., flavin adeninedinucleotide (FAD)) with the enzyme directly. When bonded to certainenzymes, FAD can increase glucose specificity and substantially lower oreliminate reactivity to other sugars. FAD also increases the temperaturestability of the enzyme compared to earlier GDHs. With the GDH-FADco-enzyme, e.g., within the enzyme domain, glucose-specific sensors maybe created that are oxygen-independent.

The GDH enzyme uses an electron acceptor and these have classically beenpotassium ferrocyanide, dichlorophenylindophenol (DCPIP) or methyleneblue. In tailoring the use of these enzymes and acceptors to the system,a polyurethane encapsulation technique may be employed via dissolutionwithin a polyurethane dispersion, and then the subsequent curing mayutilize a cross-linker for the polyurethane dispersion, to bind anelectron acceptor like a ferredoxin into the polyurethane.Alternatively, the polyurethane may itself be modified with regionsincorporating one of the electron acceptors, e.g., potassiumferrocyanide, as the cross-linker, such that it could be used as anelectron acceptor as well.

Alternatively, a hybrid dual enzyme system may be employed, in whichboth the GDH-FAD and GOx enzymes are incorporated into the enzymedomain. Such a hybrid dual enzyme system could be tailored to functionmore robustly than either enzyme system alone. For example, when anoxygen deficit arises that would ordinarily cause low oxygen performanceissues within the GOx system, the GDH-FAD system may take over, therebyallowing the overall sensor system to still function normally andprovide accurate data.

Besides enzymatic methods of measuring glucose and other analyteconcentrations, systems and methods according to present principles mayalso employ non-enzymatic methods. For example, colorimetric,fluorometric, and electrochemical sensors may be employed in this way.As one example, boronic acid-based electrochemical sensors have beenemployed in the detection of glycoproteins, as well as dopamine. Thesedo not rely on enzymes but rather on the boronic acid-diol interaction.Such methods have also shown sensitivity to glucose, and thus such andother like methods may be employed for the detection and quantificationof certain analyte concentrations, including glucose.

As noted above in the application of weight loss optimization,measurement of ketone bodies may often be employed to measuremetabolism, and in some cases the same can also be used as a secondaryindicator of diabetes (ketone bodies are elevated in blood followingfasting and episodes of hypoglycemia). Examples of endogenous ketonebodies to be continuously measured by the sensor are acetone,acetoacetic acid, and beta-hydroxybutyric acid, which are threewater-soluble molecules that are produced by the liver from fatty acidsduring periods of low food intake or carbohydrate restriction for cellsof the body to use as energy instead of glucose. Measurements of ketonebodies may be useful individually as well as in combination with glucosemeasurements to provide an indicator of diabetic control.

In certain embodiments, the sensor employs an electrochemical mechanismfor measuring ketone. For example, the sensor may comprise an enzymelayer that has an oxidoreductase enzyme (e.g., NADP dependent alcoholaldehyde/ketone oxidoreductase) that reacts with ketone bodies toproduce hydrogen peroxide, which is then oxidized by an electrode.Alternatively, through the use of a dehydrogenase enzyme (e.g.,3-hydroxybuturate dehydrogenase or NADPH alcohol dehydrogenase), ketonebodies (e.g., beta-hydroxybutyric acid) can be reacted to generate areduced electron mediator that can then travel to an electrode to createan electrochemical signal. The enzymes described herein may becross-linked and/or stabilized and then immobilized in aqueouspolyurethane, silicone, or hydrogel materials.

In some embodiments, the sensor has a ketone-specific diffusionresistance layer. A ketone-specific resistance layer may also beconstructed by appropriate modification of the diffusion resistancelayer noted above. For example, modification may be made of theabove-noted polyurethane, silicone, or epoxy membrane systems. In manycases, a polyamide membrane system may be used as limited or noalternative pathways exist for membrane transport in such materials.Improved control of ketone permeability in these membranes may beenabled by control of hydrophilic channels within these membranes, aswell as by control of the degree of membrane channel hydration andhydrogen bonding (PVP systems). That is, by control of such hydrophilicchannels, e.g., size, density, structure, configuration, and the like,control may be exerted over the ketone permeability. Ketone membranecontrol may further be enabled by modifying or using a hybrid PEO/PVPhydrophile co-system for permeability across these membranes.

Various sports and fitness optimization routines are discussed above,and the same may benefit in certain programs as called for by flowchartsof FIGS. 1, 37-45, 47, 51, and 53, by the sensing and measurement ofcertain hormones related to workouts and stress, e.g., testosterone,cortisol, epinephrine, norepinephrine, insulin, and so on. Inparticular, cortisol is a stress hormone that is released when the bodyis stressed to physical exertion or emotional stress. Testosterone is ahormone that has significant implications on sports medicine, as well ason physical and emotional status. Real-time or retrospective monitoringof cortisol and/or testosterone, intermittently or continuously, mayprovide important information on the status of the body under physicaland emotional stress. In addition, the ratio of the two parameters mayalso yield pertinent information. Such sensors can be used forperformance athletics to optimize exercise regimens, monitor doping, andoptimize muscle building or repair. Besides applications in sportsoptimization, such sensors may also be used for diagnosis and treatmentof stress-related diseases, including chronic fatigue syndrome,irritable bowel syndrome, and post-traumatic stress disorder. The devicemay further be employed by the general population to monitor biochemicalsignals that reflect stress levels and impact an individual's mood,personality, and decision-making.

In one implementation, systems and methods according to presentprinciples take such measurements and display the results in real timeto a user. In this way, and using the programmatic methods describedabove with respect to flowcharts of FIGS. 1, 37-45, 47, 51, and 53, thesystems and methods can be employed to drive behavioral change and adaptand/or personalize medical treatment.

Several sensing technologies can be employed to measure testosterone andcortisol. Such technologies include immunoelectrochemical sensors,specifically using alkaline phosphatase enzyme and cortisol antigen withdetector antibodies. Direct attachment of antibodies on electrochemicalsurfaces can be used as an amperometric sensor. Antibodies—antigenbinding can also be detected using impedance spectroscopy onelectrochemical electrodes. Alternatively, optical detection methods canbe used such as surface plasmon resonance with molecularly-imprintedfilms, fluorescence, FRET, Raman spectroscopy, and the like.

As noted above with respect to flowcharts of FIGS. 1, 37-45, 47, 51, and53, in some implementations multiple analytes (or other physiologicalparameters, including heart rate, movement, motion, temperature, skinconductivity, and so on) may be monitored at one time. In some cases themultiple measurements may occur via separate sensors, each coupled to acommon monitoring device. In other cases multiple measurements may occurvia an array of sensors. One exemplary array of sensors is illustratedin FIG. 58, which illustrates an array 750 of sensors 764 on a framework762. Additional details of such arrays of sensors may be found in U.S.Pat. Publ. No. 2011/0077490-A1, herein incorporated by reference in itsentirety.

In the embodiment of FIG. 58, the individual sensor devices 764 are eachattached to a laminate 762 that may comprise sensor electronics, and arethereby grouped together to form a sensor array 750. Alternatively oradditionally, the laminate 762 may comprise a transmitter configured totransmit sensor data to a remote computer system. Transmission of sensordata to a remote computer system can be performed wirelessly oralternatively via a tether that provides an electrical connectionbetween the sensor and the sensor electronics unit. The laminate 762 maycomprise a plurality of layers, including an adhesive layer, foradhering the laminate to the skin. In certain embodiments, the laminate762 and the sensor array 750 may be disposable and configured for singleuse.

In certain embodiments, the sensor devices of the sensor array maydiffer in a variety of characteristics, other than a difference in theinsertion site. For example, some of the sensor devices of the array maybe configured to measure glucose, while other sensor devices of the samearray may be configured to measure one or more other analytes. Forexample, one or more of the sensor devices 764 of the array 750 may beconfigured to detect glucose, while other sensor devices 764 may beconfigured to detect and measure lactic acid, ketones, testosterone,cortisol, uric acid, or other analytes.

The information collected from the different sensor devices 764, inturn, may be processed, e.g., as input in an algorithm to perform ortrigger calibration, to update calibration, and/or to validate or rejectinaccurate reference analyte values, and data to generate an analyteconcentration value that can be displayed to the user. Other informationthat may be collected include those corresponding to parameters that canaffect sensor characteristics (e.g., sensor sensitivity or baseline). Insome embodiments, different sensor devices of the sensor array may beconfigured to penetrate the skin at different preselected depths andreside in different layers (e.g., the stratum germinativum, dermis,subcutaneous layers) of the skin. For example, in some embodiments, theglucose sensor device measures at a first depth (which is ideal forglucose measurement), while a lactate sensor device measures at a seconddepth (which is ideal for lactate measurement), wherein the first andsecond depths are different.

In some embodiments, the plurality of sensor devices 764 are used toprovide information regarding differences of a certain parameter along aplane of an area covered by the sensor array 750. The parameter may berelated to physiological information, such as analyte concentration, sothat an analyte concentration gradient can be measured. In one example,the sensor array 750 may be configured to detect a buildup of lacticacid in a certain locality (e.g. in certain muscles) as a result ofexercise. Knowledge of lactic acid levels can allow a person (e.g., anathlete competing in a long distance running event) to determine and seta target pace (e.g., a certain running pace to achieve a goal time). Asanother example, the parameter may be related to the concentration of adrug, so that the body absorption rate of a drug can be determined.

In one implementation of an array of sensors, a system may be employedfor continuous metabolic monitoring for the purpose of monitoring weightloss and gain. The sensing system may contain a sensor or array ofsensors that monitors metabolic indicators that are influenced by thebody state of fat build up or reduction. The monitors may indicate ifthe body's caloric intake was more than that used or if the body wasburning more calories than consumed, e.g., where one sensor of the arraymeasures insulin and another glucose. In this way, such a device may beemployed for real-time information on the effectiveness of diets orexercise. The same can be employed as a behavior modification device ormotivational tool for patients who are actively managing their health inaccordance with, e.g., the flowcharts of FIGS. 45 and 51.

In a variation, the array may be a microneedle array where there is anarray of sensors with one common mounting unit, rather than multiplemounting units. Calibration may be performed based on one sensor butneed not be—each sensor may have its own calibration technique. In aparticular implementation, all sensors are factory calibrated.

As noted above in connection with FIGS. 8 and 9, and for a particularexample of metabolic monitoring, a simple readout may be provided wherea threshold of neutral body fat metabolism is displayed and an indicatormay be above the line if body fat is being gained or below if reduced.The distance above or below the line may indicate the magnitude or rateof energy storage or reduction. A trend graph may also be employed toindicate the amount and length of time of increasing or decreasingmetabolism. Systems and methods according to present principles mayallow for integrating the area under the curve to get a measure of totalfat build up or reduction over time. The display may read out in realtime or data may be stored for retrospective download and analysis.

Using information gained from an array of such sensors, systems andmethods according to present principles may modify user behavior bycommunicating a patient's caloric intake and energy burn, and maymotivate the user to balance that ratio or further reduce caloric intakeor increase exercise (if the objective is to lose weight). The systemsand methods may employ the programmatic functionality noted above, anduse of such systems and methods may be of significant use in educating auser on the effects of food and exercise (and other variables) on theirhealth, particularly their weight and fitness.

Particular analytes of interest measurable by the array of FIG. 58 mayinclude those noted as useful above, e.g., involved in the conversion offat into food storage or intermediaries between dietary inputs and fat,e.g., glucagon, insulin and other hormones involved in metabolicprocesses, glucose, glycogen, starch, free fatty acids, triglycerides,monoglycerides, troponin, cholesterol, proteins involved in fat storage,glycerol, pyruvate, lipids, and other carbohydrates. Other potentialcandidates for measurement include those involved in breaking down fat,e.g., glucagon, acetyl Co A, triglycerides, fatty acids, intermediariesin the citric acid cycle, ketone bodies such as acetone, acetoaceticacid and beta hydroxybutyric acid, lactate, and the like. Even furtherother potential candidates for measurement include molecules involved inaerobic or anaerobic metabolic pathways.

For weight loss optimization, sports optimization, as well as otherprograms as described above with respect to flowcharts of FIGS. 1,37-45, 47, 51, and 53, various other analytes of interest will also beunderstood to be useful measures. For example, and as noted above,sensors may be employed in the measurement of lactate and/or lacticacid, particularly for health and fitness applications, in which thepeak performance of an athlete is to be optimized. For example, oneimplementation of a body-worn lactate sensor may be similar to the CGMsystem described here but where the glucose oxidase is replaced withlactate oxidase. A small wire may go under the skin to recordinterstitial lactate levels. Alternatively, the lactate sensor could benoninvasive, and embodied by a patch such as for a sweat detector or anoptical sensor, e.g., using near IR.

Such measurements may also be employed in the determination andquantification of a level of fat burning, calorie burning, and the like.Other useful sensors include cholesterol sensors, and sensors that trackchemicals that in turn tract ingestion of medications. Such sensors maybe employed to track compliance with the medication regimen, and thesame may provide a signal indicating if a user has ingested the propermedication. Other sensors may also be employed, including those thatmeasure: blood urea, adiponectin, nitrogen, bicarbonate, oxidizingspecies, oxidants, glycerol, free fatty acids, biochemical signals ofmental health, body temperature, ICF (indicative of pancreas function),and so on.

Calibration

Calibration is the process of determining the relationship between themeasured sensor signal and the analyte concentration in clinical units.Current CGM systems require finger stick calibrations and/orconfirmations to ensure the accuracy of the system, e.g., for use indosing. Alternative methods of calibration may be used that are stillsufficient to meet the needs of the broader type II population, whichmay not require the same accuracy, or the same resolution of analyteconcentration information, at least because users are not basingmedicament dosing on such information. Rather, and as noted by theprogram-based flowcharts above, systems and methods according to presentprinciples are more generally directed toward education of users andbehavior modification.

Additional data may be employed to help the calibration as well. Forexample, if the user is a diabetic and is measuring their blood glucoseseveral times a day anyway, such values may be employed as calibrationvalues. In some cases it is not necessary to hone in on a particularvalue. Determination that a user is in a range of values may besufficient for type II users. The range may be determined and used inproviding the user with information about whether goals are being met,or other information about the program they are on.

Systems and methods according to present principles may further beemployed to use calibration information about one sensor to calibrateanother, e.g., an adjacent sensor, e.g., one under the same membrane.Such calibration may be performed, as drift parameters, if caused by themembrane, may be assumed to be the same for both sensors. For example,if both sensors are under the same membrane layer, e.g., a glucosesensor and a lactate sensor, and if one or more calibration parameterswere determined ex vivo, then the calibration parameters may be assumedto bear a similar relationship in vivo, and thus the measurement of onecan be used to determine the other. For example, if the lactate sensorhas a known offset in calibration from the glucose sensor (or otherrelationship or scaling or correlation factor), as measured ex vivo,then in vivo, a determination of calibration for that glucose sensor maybe employed to calibrate the lactate sensor. For example, if calibrationof the glucose sensor is seen to drift by 50%, then the calibration ofthe lactate sensor may be assumed to have drifted by 50%. Consequently,an update of one or more calibration parameters of one sensor can resultin the update of one or more calibration parameters of the other sensor.

Additional details of such aspects may be seen in U.S. Pat. Publ. No.2010/0198035, U.S. Pat. Publ. No. 2011/0004085; and U.S. Pat. Publ. No.2011/0024307, each of which is incorporated herein by reference in itsentirety.

In addition, systems and methods according to present principles may usefactory calibration information to start and then incorporate theautomatic calibration technique over time to get more accurate glucoseinformation. If the signal did not follow pre-prescribed parameters, orwas outside of pre-prescribed parameters, the system could request acalibration value using known techniques, e.g., SMBG or finger stickcalibrations. The systems and methods may then incorporate this glucoseinformation into the original parameters to adjust the setpoint from,e.g., 100 mg/dL, to a more appropriate and accurate value. That is,while the above techniques intended for use in type II applications maybe generally configured to avoid the need for finger stick calibrations,if such are available, systems and methods according to presentprinciples may apply the same advantageously, for calibration purposesand otherwise.

Systems and methods according to present principles may be configured todetermine a confidence level or range, and as the resolution or accuracyof the data changes, the confidence level or range can change. In moredetail, the display could generate a value and a trend graph or the samemay show a range or other UI element, as described in greater detailabove. The range may also change over time and shrink or expand as aconfidence in the accuracy changes. For instance, during initialwarm-up, a factory calibration value may be utilized. However, itsaccuracy may not be as precise as it would be with additionalinformation. During this time, the display may show a range rather thana value.

A further feature of systems and methods according to present principlesare that they may request information when a user is set up within thesystem and adjust which technique to use depending on the information.For example, the system may prompt the user to enter whether they havetype I diabetes, type II diabetes, or are nondiabetic, and may select adifferent technique depending upon the answer. Systems and methodsaccording to present principles may further ask if the user isinterested in weight loss optimization, sports and fitness optimization,or other like optimization routine, and may adjust the algorithmaccordingly. The device may also, e.g., be used in a “blinded” mode foran extended period of time, e.g., 14 days, and only accept blood glucosevalues. These blood glucose values could be used to learn what apatient's resting blood glucose is, which could better guide theassumption of the auto calibration blood glucose value. After theextended period of time, the user could then use the device in autocalibration mode.

Other techniques may also be employed to simplify the calibration ofsensors. In particular, where multiple sensors are employed, e.g., in amulti-sensor system such as that described in FIG. 58, the calibrationof one sensor can be based on the calibration of another. This techniquemay be distinguished from that described above, where the calibrationparameters are determined, and knowledge of their relationship ex vivoleads to knowledge of one implying knowledge of the other in vivo. Inthis former implementation, two analytes are monitored where one iseasier to calibrate than the other. For example, the calibration of aglucose sensor may be based on that of a different analyte of interest,e.g., triglycerides, lactate, ketones, and so on. In a particularembodiment, tears, saliva, interstitial fluid, etc., may be employed assuch alternative analytes. Conversely, the calibration of alternateanalyte sensors may be based on a glucose calibration.

In general, in such systems, at least two analytes are monitored. One iseasier to calibrate than the other. If there is a known relationshipbetween the two analytes' calibrations or sensitivities, then thecalibration of the sensor that is more difficult to calibrate can bebased on the one that is easier to calibrate. In other words,calibration parameters of an analyte that is relatively easy tocalibrate are determined and then used to estimate the calibrationparameters of an analyte that is more difficult to calibrate. In somecases these involve steps of determining or estimating the sensitivityof the sensor. In a particular implementation, an analyte that isrelatively easy to calibrate, such as glucose, is used to estimate acalibration (or sensitivity) of an analyte sensor that is relativelydifficult to calibrate, such as lactate or lactic acid.

It is noted that in most cases the calibration-simplifying routinesnoted above with respect to glucose sensors may also be employed forother analyte sensors.

What has been described above are systems and methods forprogrammatically educating users about disease states, e.g., educatingtype II diabetic users about how to better manage their diabetes. Forexample, systems and methods have been described in which programs areprescribed for and/or selected by users, including where such programsare suggested based on recognized patterns. Variations and complementaryroutines will also be understood. For example, applications noted abovemay help coordinate and record subject user's actions during clinicalstudies. For example, if a clinical study requires one day with sevenfinger sticks at 30 minute intervals, then the schedule could beprogrammed into a digital calendar with alarms to remind the subjectuser throughout the study. The reminders may be displayed along withprogram reminders, or otherwise in addition thereto. Also, if thesubject user's glucose spikes above a certain threshold, the app couldtrigger a voluntary survey which asks the subject user what they ate,what activities they took part in, or a request for another finger stickfor more thorough analysis later. Such a survey may be performed inconjunction with the “discovery mode” discussed above. If the systemsand methods are detecting extreme noise or any other data artifacts,then the same can immediately survey the subject user before the eventis forgotten. Such systems and methods help ensure desired participationwith clinical trials, e.g., ensuring finger sticks at the right timesand intervals, as well as ensuring logging of daily events such asshowers and actively capturing finger sticks during excursion events.The systems and methods may also help build a better record of pairedevents, which could be used to correlate user actions to specific dataartifacts. Capturing finger sticks during excursions is particularlyimportant for certain sensor performance metrics, such as sensitivity,baseline, and time lag. Such may be especially true for nondiabeticpatients who have excursion events at lower frequencies and over shorterperiods.

In another potential variation, systems and methods according to presentprinciples may be used as a diagnostic, e.g., used in diagnostic teststo determine diabetes or other disease states, or to determine a levelof progression of such disease states. That is, and in one example,systems and methods according to present principles may be able toprovide an indication of the level of diabetes, as well as stages andseverity thereof. Greater resolution may similarly be obtained on theprogress of the disease, or the stage of progression or severity, ascompared to prior metrics.

In yet another variation, a recommendation can be triggered to suggest auser see a physician if glucose (or other analyte) data is outside ofset criteria. That is, for either diabetic or non-diabetic users, orusers with other diseases or medical challenges, criteria may beprogrammed (which criteria may vary depending on user and/or condition)and if an appertaining analyte value meets the criteria, e.g., achievesa predetermined threshold, then an alert may be displayed. Similarly,patients may be alerted if relevant measured values are outside of arange or at a percentile in a distribution of individuals. The alert mayprovide advice on correction, e.g., to eat better, to eat certain foods,or may similarly recommend certain diets or exercise.

In yet another variation, CGM traces and other inputs (passive oractive) may be employed to detect onset or progression of variouscomplications, e.g., using CGM tracing to detect worsening of renalfailure, sepsis, infection, flu, stress, sleep deprivation, shiftworkpatterns for medication adjustment, and so on.

The connections between the elements shown in the figures illustrateexemplary communication paths. Additional communication paths, eitherdirect or via an intermediary, may be included to further facilitate theexchange of information between the elements. The communication pathsmay be bi-directional communication paths allowing the elements toexchange information.

As used herein, the term “determining” is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a wide variety of actions.For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Also, any connectionis properly termed a computer-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray® disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Thus, in some aspects a computer-readable medium may comprisenon-transitory computer-readable medium (e.g., tangible media). Inaddition, in some aspects a computer-readable medium may comprisetransitory computer-readable medium (e.g., a signal). Combinations ofthe above should also be included within the scope of computer-readablemedia.

The methods disclosed herein comprise one or more steps or actions forachieving the described methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Certain aspects may comprise a computer program product for performingthe operations presented herein. For example, such a computer programproduct may comprise a computer-readable medium having instructionsstored (and/or encoded) thereon, the instructions being executable byone or more processors to perform the operations described herein. Forcertain aspects, the computer program product may include packagingmaterial.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained. For example, a device can becoupled to a server to facilitate the transfer of means for performingthe methods described herein. Alternatively, various methods describedherein can be provided via storage means (e.g., RAM, ROM, a physicalstorage medium such as a compact disc (CD) or floppy disk, etc.), suchthat a user terminal and/or base station can obtain the various methodsupon coupling or providing the storage means to the device. Moreover,any other suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

The system and method may be fully implemented in any number ofcomputing devices. Typically, instructions are laid out on computerreadable media, generally non-transitory, and these instructions aresufficient to allow a processor in the computing device to implement themethod of the invention. The computer readable medium may be a harddrive or solid state storage having instructions that, when run, areloaded into random access memory. Inputs to the application, e.g., fromthe plurality of users or from any one user, may be by any number ofappropriate computer input devices. For example, users may employ akeyboard, mouse, touchscreen, joystick, trackpad, other pointing device,or any other such computer input device to input data relevant to thecalculations. Data may also be input by way of an inserted memory chip,hard drive, flash drives, flash memory, optical media, magnetic media,or any other type of file-storing medium. The outputs may be deliveredto a user by way of a video graphics card or integrated graphics chipsetcoupled to a display that maybe seen by a user. Alternatively, a printermay be employed to output hard copies of the results. Given thisteaching, any number of other tangible outputs will also be understoodto be contemplated by the invention. For example, outputs may be storedon a memory chip, hard drive, flash drives, flash memory, optical media,magnetic media, or any other type of output. It should also be notedthat the invention may be implemented on any number of different typesof computing devices, e.g., personal computers, laptop computers,notebook computers, net book computers, handheld computers, personaldigital assistants, mobile phones, smart phones, tablet computers, andalso on devices specifically designed for these purpose. In oneimplementation, a user of a smart phone or WiFi-connected devicedownloads a copy of the application to their device from a server usinga wireless Internet connection. An appropriate authentication procedureand secure transaction process may provide for payment to be made to theseller. The application may download over the mobile connection, or overthe WiFi or other wireless network connection. The application may thenbe run by the user. Such a networked system may provide a suitablecomputing environment for an implementation in which a plurality ofusers provide separate inputs to the system and method. In the belowsystem where multiple types of data are contemplated, the plural inputsmay allow plural devices or users to input relevant data at the sametime.

What is claimed is:
 1. A method of alerting a user to a pattern, andproviding a program to address the pattern, comprising: a. evaluatinguser data to determine a pattern; a. comparing the pattern against acriterion to determine if the determined pattern is a pattern for whichimprovement is desired; b. determining a program to improve the pattern;c. monitoring and storing analyte concentration data of the user; d.analyzing the monitored and stored analyte concentration data of theuser; e. evaluating the analyzed analyte concentration data of the useragainst the determined program; and f. displaying an output responsiveto the evaluating step.
 2. The method of claim 1, wherein thedetermining a program to improve the pattern further comprises: a.determining a set of potential programs to improve the pattern; b.displaying a user interface, the user interface including one or moregraphical elements respectively representing the set of potentialprograms; c. receiving a selection of one of the potential programs fromthe user interface, d. wherein the determined program is defined as thereceived selection.
 3. The method of claim 1, wherein the displaying auser interface further comprises analyzing retrospective data of a userand basing the user interface at least in part on the analysis.
 4. Themethod of claim 1, further comprising, after the step of determining aprogram, displaying an indication on the user interface, the indicationrepresenting initial guidance for following the determined program. 5.The method of claim 4, wherein the displaying an indication includes: a.displaying suggested meals, foods, or recipes, helpful in performing theprogram; and/or b. displaying suggested exercise routines helpful inperforming the program.
 6. The method of claim 1, wherein the monitoringand storing is performed at least in part by a continuous analytemonitor.
 7. The method of claim 1, further comprising monitoring otherdata about the user, and wherein the analyzing or evaluating steps, orboth, are based on the analyte concentration data and the other data. 8.The method of claim 7, wherein the other data includes activity data. 9.The method of claim 8, wherein the activity data is received from anaccelerometer or a GPS device.
 10. The method of claim 8, wherein theactivity data indicates an activity level, and wherein the activitylevel is selected from the group consisting of: sleeping, sedentary,light activity, medium activity, or strenuous activity.
 11. The methodof claim 1, wherein the criteria is received from a cloud connectedsource.
 12. The method of claim 8, wherein the evaluating includesevaluating an effect of the activity data on the analyte concentrationdata.
 13. The method of claim 12, wherein the displaying includesdisplaying the effect of the activity data on the analyte concentrationdata.
 14. The method of claim 7, wherein the other data include dataabout other analytes.
 15. The method of claim 14, wherein the otheranalytes are selected from the group consisting of: ketones, lacticacid, lactate, glycerol, triglycerides, cortisol, and testosterone. 16.The method of claim 15, wherein the analyte concentration data ismeasured by an analyte sensor, and wherein the data about one or moreother analytes is received from one or more other analyte sensors, andwherein the one or more other analyte sensors are calibrated based on acalibration of the analyte sensor.
 17. The method of claim 7, whereinthe other data include meal data.
 18. The method of claim 17, whereinthe meal data is received from: a. a social network; b. user entry; c. afood app; or d. photographic data.
 19. The method of claim 1, whereinthe determined program is associated with a difficulty level, andwherein the evaluating against the determined program includesevaluating against the associated difficulty level.
 20. The method ofclaim 19, wherein the difficulty level is set by the program based atleast in part on a retrospective history of the patient.
 21. The methodof claim 19, wherein the difficulty level is selected by the user. 22.The method of claim 1, wherein the output includes a color indicating ifa goal associated with the program was met.
 23. The method of claim 1,wherein the output includes an avatar indicating if a goal associatedwith the program was met.
 24. The method of claim 1, wherein the outputincludes a trend graph indicating at least a trace signal representingthe analyte concentration value over a time period associated with theprogram.
 25. The method of claim 24, wherein the trend graph furtherincludes a desired analyte concentration value or range of values overthe time period.
 26. The method of claim 25, wherein the desired analyteconcentration value or range of values is based on a modeled, ideal, orpredicted analyte concentration value or range of values.
 27. The methodof claim 25, wherein the determined program is associated with adifficulty level, and wherein the desired analyte concentration value orrange of values is further based on the difficulty level.
 28. The methodof claim 1, wherein the evaluated user data includes retrospectiveanalyte concentration data.
 29. The method of claim 1, wherein theevaluated user data includes retrospective meal data.
 30. The method ofclaim 1, wherein the displaying an output includes displaying anindicator of the determined pattern.
 31. The method of claim 1, whereinthe determined pattern is selected from the group consisting of:overnight lows, postprandial spikes, a type of discriminated fault, apattern of high analyte variability, a consistent pattern of weeklyhighs or lows.
 32. The method of claim 1, further comprising determininga baseline analyte concentration pattern for the user, and wherein thedetermined pattern is a consistent variation from the baseline pattern.33. The method of claim 1, wherein the evaluating user data to determinea pattern step further comprises initiating a discovery mode, wherein inthe discovery mode, one or more questions are posed on the userinterface, user responses to the one or more questions constitutingadditional user data, and wherein the evaluating user data step furthercomprises evaluating the additional user data along with the monitoredand stored analyte concentration data to determine a pattern.
 34. Themethod of claim 33, wherein the additional user data is meal data. 35.The method of claim 33, wherein the additional user data is activitydata.
 36. The method of claim 1, further comprising transmitting theoutput to a cloud connected entity.